Combinatorial Game Theory Workshop
40_game_theory_ch_2
Chapter2Nim and combinatorial games2.1Aims of the chapterThis chapter•introduces the basics of combinatorial games,and explains the central role of the game nim.A detailed summary of the chapter is given in section2.5.Furthermore,this chapter•demonstrates the use of abstract mathematics in game theory.This chapter is written more formally than the other chapters,in parts in thetraditional mathematical style of definitions,theorems and proofs.One reason fordoing this,and why we start with combinatorial games,is that this topic and styleserves as a warning shot to those who think that game theory,and this unit inparticular,is‘easy’.If we started with the well-known‘prisoner’s dilemma’(whichmakes its due appearance in Chapter3),the less formally inclined student might belulled into a false sense of familiarity and‘understanding’.We therefore startdeliberately with an unfamiliar topic.This is a mathematics unit,with great emphasis on rigour and clarity,and on usingmathematical notions precisely.As mathematical prerequisites,game theory requiresonly the very basics of linear algebra,calculus and probability theory.However,gametheory provides its own conceptual tools that are used to model and analyseinteractive situations.This unit emphasises the mathematical structure of theseconcepts,which belong to‘discrete mathematics’.Learning a number of newmathematical concepts is exemplified by combinatorial game theory,and it willcontinue in the study of classical game theory in the later chapters.2.2Learning objectivesAfter studying this chapter,you should be able to:•play nim optimally;•explain the concepts of game-sums,equivalent games,nim values and the mex rule;•apply these concepts to play other impartial games like those described in the exercises.40Game theory2.3Essential readingThis chapter of the guide.2.4Further readingVery few textbooks on game theory deal with combinatorial games.An exception ischapter1of the following book:•Mendelson,Elliot Introducing Game Theory and Its Applications.(Chapman& Hall/CRC,2004)[ISBN1584883006].The winning strategy for the game nim based on the binary system wasfirst describedin the following article,which is available electronically from the JSTOR archive:•Bouton,Charles‘Nim,a game with a complete mathematical theory.’The Annals of Mathematics,2nd Ser.,Vol.3,No.1/4(1902),pp.35–39.The definitive text on combinatorial game theory is the set of volumes‘WinningWays’by Berlekamp,Conway and Guy.The material of this chapter appears in thefirst volume:•Berlekamp,Elwyn R.,John H.Conway and Richard K.Guy Winning Ways for Your Mathematical Plays,Volume1,second edition.(A.K.Peters,2001)[ISBN1568811306].Some small pieces of that text have been copied here nearly verbatim,for example inSections2.7,2.9,and2.12below.The four volumes of‘Winning Ways’are beautiful books.However,they are notsuitable reading for a beginner,because the mathematics is hard,and the reader isconfronted with a wealth of material.The introduction to combinatorial game theorygiven here represents a very small fraction of that body of work,but may invite youto study it further.A very informative and entertaining mathematical tour of parlour games is•Bewersdorff,J¨o rg Logic,Luck and White Lies.(A.K.Peters,2005)[ISBN 1568812108].Combinatorial games are treated in part II of that book.2.5What is combinatorial game theory?This chapter is on the topic of combinatorial games.These are games with twoplayers,perfect information,and no chance moves,specified by certain rules.Familiargames of this sort are chess,go,checkers,tic-tac-toe,dots-and-boxes,and nim.Suchgames can be played perfectly in the sense that either one player can force a win orboth can force a draw.In reality,games like chess and go are too complex tofind anoptimal strategy,and they derive their attraction from the fact that(so far)it is notknown how to play them perfectly.We will,however,learn how to play nim perfectly.There is a‘classical’game theory with applications in economics which is verydifferent from combinatorial game theory.The games in classical game theory aretypically formal models of conflict and co-operation which cannot only be lost orWhat is combinatorial game theory? won,and in which there is often no perfect information about past and future moves.To the economist,combinatorial games are not very interesting.Chapters3–6of theunit are concerned with classical game theory.Why,then,study combinatorial games at all in a unit that is mostly about classicalgame theory,and which aims to provide an insight into the theory of games as usedin economics?The reason is that combinatorial games have a rich and interesting mathematical theory.We will explain the basics of that theory,in particular thecentral role of the game nim for impartial games.It is non-trivial mathematics,it isfun,and you,the student,will have learned something that you would most likelynot have learned otherwise.Thefirst‘trick’from combinatorial game theory is how to win in the game nim,using the binary system.Historically,that winning strategy was discoveredfirst(published by Charles Bouton in1902).Only later did the central importance of nim,in what is known as the Sprague–Grundy theory of impartial games,becomeapparent.It also revealed why the binary system is important(and not,say,theternary system,where numbers are written in base three),and learning that is more satisfying than just learning how to use it.In this chapter,wefirst define the game nim and more general classes of games withperfect information.These are games where every player knows exactly the state ofthe game.We then define and study the concepts listed in the learning outcomesabove,which are the concepts of game-sums,equivalent games,nim values and themex rule.It is best to learn these concepts by following the chapter in detail.Wegive a brief summary here,which will make more sense,and should be re-consulted,after afirst study of the chapter(so do not despair if you do not understand this summary).Mathematically,any game is defined by other‘games’that a player can reach in hisfirst move.These games are called the options of the game.This seemingly circulardefinition of a‘game’is sound because the options are simpler games,which needfewer moves in total until they end.The definition is therefore not circular,butrecursive,and the mathematical tool to argue about such games is that ofmathematical induction,which will be used extensively(it will also recur inchapter3as‘backward induction’for game trees).Here,it is very helpful to befamiliar with mathematical induction for proving statements about natural numbers.We focus here on impartial games,where the available moves are the same nomatter whether player I or player II is the player to make a move.Games are‘combined’by the simple rule that a player can make a move in exactly one of thegames,which defines a sum of these games.In a‘losing game’,thefirst player tomove loses(assuming,as always,that both players play as well as they can).Animpartial game added to itself is always losing,because any move can be copied inthe other game,so that the second player always has a move left.This is known asthe‘copycat’principle(lemma2.6).An important observation is that a losing gamecan be‘added’(via the game-sum operation)to any game without changing thewinning or losing properties of the original game.In section2.11,the central theorem2.10explains the winning strategy in nim.The importance of nim for impartial games is then developed in section2.12via thebeautiful mex rule.After the comparatively hard work of the earlier sections,wealmost instantly obtain that any impartial game is equivalent to a nim heap(corollary2.13).At the end of the chapter,the sizes of these equivalent nim heaps(called nim values)are computed for some examples of impartial games.Many other examples arestudied in the exercises.40Game theoryOur exposition is distinct from the classic text‘Winning Ways’in the followingrespects:First,we only consider impartial games,even though many aspects carryover to more general combinatorial games.Secondly,we use a precise definition ofequivalent games(see section2.10),because a game where you are bound to loseagainst a smart opponent is not the same as a game where you have already lost.Two such games are merely equivalent,and the notion of equivalent games is helpfulin understanding the theory.So this text is much more restricted,but to some extentmore precise than‘Winning Ways’,which should help make this topic accessible andenjoyable.2.6Nim–rulesThe game nim is played with heaps(or piles)of chips(or counters,beans,pebbles,matches).Players alternate in making a move,by removing some chips from one ofthe heaps(at least one chip,possibly the entire heap).Thefirst player who cannotmove any more loses the game.The players will be called,rather unimaginatively,player I and player II,with player Ito start the game.For example,consider three heaps of size1,1,2.What is a good move?Removingone of the chips from the heap with two chips will create the position1,1,1,thenplayer II must move to1,1,then player I to1,and then player II takes the last chipand wins.So this is not a good opening move.The winning move is to remove allchips from the heap of size2,to reach position1,1,and then player I will win.Hence we call1,1,2a winning position,and1,1a losing position.When moving in a winning position,the player to move can win by playing well,bymoving to a losing position of the other player.In a losing position,the player tomove will lose no matter what move she chooses,if her opponent plays well.Thismeans that all moves from a losing position lead to a winning position of theopponent.In contrast,one needs only one good move from a winning position thatgoes to a losing position of the next player.Another winning position consists of three nim heaps of sizes1,1,1.Here all movesresult in the same position and player I always wins.In general,a player in a winningposition must play well by picking the right move.We assume that players play well,forcing a win if they can.Suppose nim is played with only two heaps.If the two heaps have equal size,forexample in position4,4,then thefirst player to move loses(so this is a losingposition),because player II can always copy player I’s move by equalising the twoheaps.If the two heaps have different sizes,then player I can equalise them byremoving an appropriate number of chips from the larger heap,putting player II in alosing position.The rule for2-heap nim is therefore:Lemma2.1The nim position m,n is winning if and only if m=n,otherwise losing,for all m,n≥0.This lemma applies also when m=0or n=0,and thus includes the cases that oneor both heap sizes are zero(meaning only one heap or no heap at all).With three or more heaps,nim becomes more difficult.For example,it is notimmediately clear if,say,positions1,4,5or2,3,6are winning or losing positions.⇒At this point,you should try exercise2.1(a)on page28.Combinatorial games,in particular impartial games 2.7Combinatorial games,in particular impartial gamesThe games we study in this chapter have,like nim,the following properties:1.There are just two players.2.There are several,usuallyfinitely many,positions,and sometimes a particularstarting position.3.There are clearly defined rules that specify the moves that either player canmake from a given position to the possible new positions,which are called theoptions of that position.4.The two players move alternately,in the game as a whole.5.In the normal play convention a player unable to move loses.6.The rules are such that play will always come to an end because some player willbe unable to move.This is called the ending condition.So there can be nogames which are drawn by repetition of moves.7.Both players know what is going on,so there is perfect information.8.There are no chance moves such as rolling dice or shuffling cards.9.The game is impartial,that is,the possible moves of a player only depend onthe position but not on the player.As a negation of condition5,there is also the mis`e re play convention where a playerunable to move wins.In the surrealist(and unsettling)movie‘Last year atMarienbad’by Alain Resnais from1962,mis`e re nim is played,several times,withrows of matches of sizes1,3,5,7.If you have a chance,try to watch that movie andspot when the other player(not the guy who brought the matches)makes a mistake!Note that this is mis`e re nim,not nim,but you will be able tofind out how to play itonce you know how to play nim.(For games other than nim,normal play and mis`e reversions are typically not so similar.)In contrast to condition9,games where the available moves depend on the player(as in chess where one player can only move white pieces and the other only blackpieces)are called partisan games.Much of combinatorial game theory is aboutpartisan games,which we do not consider to keep matters simple.Chess,and the somewhat simpler tic-tac-toe,also fail condition6because they mayend in a tie or draw.The card game poker does not have perfect information(asrequired in7)and would lose all its interest if it had.The analysis of poker,althoughit is also a win-or-lose game,leads to the‘classical’theory of zero-sum games(withimperfect information)that we will consider later.The board game backgammon is agame with perfect information but with chance moves(violating condition8)because dice are rolled.We will be relatively informal in style,but our notions are precise.In condition3above,for example,the term option refers to a position that is reachable in onemove from the current position;do not use‘option’when you mean‘move’.Similarly,we will later use the term strategy to define a plan of moves,one for everyposition that can occur in the game.Do not use‘strategy’when you mean‘move’.However,we will take some liberty in identifying a game with its starting positionwhen the rules of the game are clear.40Game theory⇒Try now exercises2.2and2.3starting on page28.2.8Simpler games and notation for nim heapsA game,like nim,is defined by its rules,and a particular starting position.Let G besuch a particular instance of nim,say with the starting position1,1,2.Knowing therules,we can identify G with its starting position.Then the options of G are1,2,and1,1,1,and1,1.Here,position1,2is obtained by removing either thefirst or thesecond heap with one chip only,which gives the same result.Positions1,1,1and1,1are obtained by making a move in the heap of size two.It is useful to list the optionssystematically,considering one heap to move in at a time,so as not to overlook anyoption.Each of the options of G is the starting position of another instance of nim,definingone of the new games H,J,K,say.We can also say that G is defined by the movesto these games H,J,K,and we call these games also the options of G(byidentifying them with their starting positions;recall that the term‘option’has beendefined in point3of section2.7).That is,we can define a game as follows:Either the game has no move,and theplayer to move loses,or a game is given by one or several possible moves to newgames,in which the other player makes the initial move.In our example,G isdefined by the possible moves to H,J,or K.With this definition,the entire game iscompletely specified by listing the initial moves and what games they lead to,because all subsequent use of the rules is encoded in those games.This is a recursive definition because a‘game’is defined in terms of‘game’itself.We have to add the ending condition that states that every sequence of moves in agame must eventually end,to make sure that a game cannot go on indefinitely.This recursive condition is similar to defining the set of natural numbers as follows:(a)0is a natural number;(b)if n is a natural number,then so is n+1;and(c)allnatural numbers are obtained in this way,starting from0.Condition(c)can beformalised by the principle of induction that says:if a property P(n)is true for n=0,and if the property P(n)implies P(n+1),then it is true for all natural numbers.We use the following notation for nim heaps.If G is a single nim heap with nchips,n≥0,then we denote this game by∗n.This game is completely specified byits options,and they are:options of∗n:∗0,∗1,∗2,...,∗(n−1).(2.1) Note that∗0is the empty heap with no chips,which allows no moves.It is invisiblewhen playing nim,but it is useful to have a notation for it because it defines themost basic losing position.(In combinatorial game theory,the game with no moves,which is the empty nim heap∗0,is often simply denoted as0.)We could use(2.1)as the definition of∗n;for example,the game∗4is defined by itsoptions∗0,∗1,∗2,∗3.It is very important to include∗0in that list of options,because it means that∗4has a winning move.Condition(2.1)is a recursivedefinition of the game∗n,because its options are also defined by reference to suchgames∗k,for numbers k smaller than n.This game fulfils the ending conditionbecause the heap gets successively smaller in any sequence of moves.If G is a game and H is a game reachable by one or more successive moves from thestarting position of G,then the game H is called simpler than G.We will oftenprove a property of games inductively,using the assumption that the property appliesto all simpler games.An example is the–already stated and rather obvious–Sums of games property that one of the two players can force a win.(Note that this applies togames where winning or losing are the only two outcomes for a player,as implied bythe‘normal play’convention in5above.)Lemma2.2In any game G,either the starting player I can force a win,or player IIcan force a win.Proof.When the game has no moves,player I loses and player II wins.Now assumethat G does have options,which are simpler games.By inductive assumption,ineach of these games one of the two players can force a win.If,in all of them,thestarting player(which is player II in G)can force a win,then she will win in G byplaying accordingly.Otherwise,at least one of the starting moves in G leads to agame G where the second-moving player in G (which is player I in G)can force awin,and by making that move,player I will force a win in G.If in G,player I can force a win,its starting position is a winning position,and wecall G a winning game.If player II can force a win,G starts with a losing position,and we call G a losing game.2.9Sums of gamesWe continue our discussion of nim.Suppose the starting position has heap sizes1,5,5.Then the obvious good move is to option5,5,which is losing.What about nim with four heaps of sizes2,2,6,6?This is losing,because2,2and6,6independently are losing positions,and any move in a heap of size2can becopied in the other heap of size2,and similarly for the heaps of size6.There is asecond way of looking at this example,where it is not just two losing games puttogether:consider the game with heap sizes2,6.This is a winning game.However,two such winning games,put together to give the game2,6,2,6,result in a losinggame,because any move in one of the games2,6,for example to2,4,can be copiedin the other game,also to2,4,giving the new position2,4,2,4.So the secondplayer,who plays‘copycat’,always has a move left(the copying move)and hencecannot lose.Definition2.3The sum of two games G and H,written G+H,is defined asfollows:The player may move in either G or H as allowed in that game,leaving theposition in the other game unchanged.Note that G+H is a notation that applies here to games and not to numbers,evenif the games are in some way defined using numbers(for example as nim heaps).The result is a new game.More formally,assume that G and H are defined in terms of their options(via movesfrom the starting position)G1,G2,...,G k and H1,H2,...,H m,respectively.Then theoptions of G+H are given asoptions of G+H:G1+H,...,G k+H,G+H1,...,G+H m.(2.2) Thefirst list of options G1+H,G2+H,...,G k+H in(2.2)simply means that theplayer makes his move in G,the second list G+H1,G+H2,...,G+H m that hemakes his move in H.We can define the game nim as a sum of nim heaps,where any single nim heap isrecursively defined in terms of its options by(2.1).So the game nim with heaps ofsize1,4,6is written as∗1+∗4+∗6.40Game theoryThe‘addition’of games with the abstract+operation leads to an interestingconnection of combinatorial games with abstract algebra.If you are somewhatfamiliar with the concept of an abstract group,you will enjoy this connection;if not,you do not need to worry,because this connection it is not essential for ourdevelopment of the theory.A group is a set with a binary operation+that fulfils three properties:1.The operation+is associative,that is,G+(J+K)=(G+J)+K holds for allG,J,K.2.The operation+has a neutral element0,so that G+0=G and0+G=G forall G.3.Every element G has an inverse−G so that G+(−G)=0.Furthermore,4.The group is called commutative(or‘abelian’)if G+H=H+G holds for allG,H.Familiar groups in mathematics are,for example,the set of integers with addition,orthe set of positive real numbers with multiplication(where the multiplicationoperation is written as·,the neutral element is1,and the inverse of G is written asG−1).The games that we consider form a group as well.In the way the sum of two gamesG and H is defined,G+H and H+G define the same game,so+is commutative.Moreover,when one of these games is itself a sum of games,for example H=J+K,then G+H is G+(J+K)which means the player can make a move in exactly one ofthe games G,J,or K.This means obviously the same as the sum of games(G+J)+K,that is,+is associative.The sum G+(J+K),which is the same as(G+J)+K,can therefore be written unambiguously as G+J+K.An obvious neutral element is the empty nim heap∗0,because it is‘invisible’(itallows no moves),and adding it to any game G does not change the game.However,there is no direct way to get an inverse operation because for any game Gwhich has some options,if one adds any other game H to it(the intention beingthat H is the inverse−G),then G+H will have some options(namely at least theoptions of moving in G and leaving H unchanged),so that G+H is not equal to theempty nim heap.The way out of this is to identify games that are‘equivalent’in a certain sense.Wewill see shortly that if G+H is a losing game(where thefirst player to move cannotforce a win),then that losing game is‘equivalent’to∗0,so that H fulfils the role ofan inverse of G.2.10Equivalent gamesThere is a neutral element that can be added to any game G without changing it.By definition,because it allows no moves,it is the empty nim heap∗0:G+∗0=G.(2.3)However,other games can also serve as neutral elements for the addition of games.We will see that any losing game can serve that purpose,provided we considercertain games as equivalent according to the following definition.Equivalent games Definition2.4Two games G,H are called equivalent,written G≡H,if and only iffor any other game J,the sum G+J is losing if and only if H+J is losing.In definition2.4,we can also say that G≡H if for any other game J,the sum G+Jis winning if and only if H+J is winning.In other words,G is equivalent to H if,whenever G appears in a sum G+J of games,then G can be replaced by H without changing whether G+J is winning or losing.One can verify easily that≡is indeed an equivalence relation,meaning it is reflexive(G≡G),symmetric(G≡H implies H≡G),and transitive(G≡H and H≡K implyG≡K;all these conditions hold for all games G,H,K).Using J=∗0in definition2.4and(2.3),G≡H implies that G is losing if and only ifH is losing.The converse is not quite true:just because two games are winning doesnot mean they are equivalent,as we will see shortly.However,any two losing gamesare equivalent,because they are all equivalent to∗0:Lemma2.5If G is a losing game(the second player to move can force a win),thenG≡∗0.Proof.Let G be a losing game.We want to show G≡∗0By definition2.4,this istrue if and only if for any other game J,the game G+J is losing if and only if∗0+Jis losing.According to(2.3),this holds if and only if J is losing.So let J be any other game;we want to show that G+J is losing if and only if J islosing.Intuitively,adding the losing game G to J does not change which player in Jcan force a win,because any intermediate move in G by his opponent is simplycountered by the winning player,until the moves in G are exhausted.Formally,wefirst prove by induction the simpler claim that for all games J,if J islosing,then G+J is losing.(So wefirst ignore the‘only if’part.)Our inductive assumptions for this simpler claim are:for all losing games G that are simplerthan G,if J is losing,then G +J is losing;and for all games J that are simplerthan J,if J is losing,then G+J is losing.So suppose that J is losing.We want to show that G+J is losing.Any initial movein J leads to an option J which is winning,which means that there is acorresponding option J of J (by player II’s reply)where J is losing.Hence,whenplayer I makes the corresponding initial move from G+J to G+J ,player II cancounter by moving to G+J .By inductive assumption,this is losing because J islosing.Alternatively,player I may move from G+J to G +J.Because G is a losinggame,there is a move by player II from G to G where G is again a losing game,and hence G +J is also losing,by inductive assumption,because J is losing.Thiscompletes the induction and proves the claim.What is missing is to show that if G+J is losing,so is J.If J was winning,then therewould be a winning move to some option J of J where J is losing,but then,by ourclaim(the‘if’part that we just proved),G+J is losing,which would be a winningoption in G+J for player I.But this is a contradiction.This completes the proof.The preceding lemma says that any losing game Z,say,can be added to a game Gwithout changing whether G is winning or losing(in lemma2.5,Z is called G).Thatis,extending(2.3),Z losing=⇒G+Z≡G.(2.4)As an example,consider Z=∗1+∗2+∗3,which is nim with three heaps of sizes1,2,3.To see that Z is losing,we examine the options of Z and show that all ofthem are winning games.Removing an entire heap leaves two unequal heaps,whichis a winning position by lemma2.1.Any other move produces three heaps,two of40Game theorywhich have equal size.Because two equal heaps define a losing nim game Z,they can be ignored by(2.4),meaning that all these options are like single nim heaps and therefore winning positions,too.So Z=∗1+∗2+∗3is losing.The game G=∗4+∗5is clearly winning.By(2.4), the game G+Z is equivalent to G and is also winning.However,verifying directly that∗1+∗2+∗3+∗4+∗5is winning would not be easy to see without using(2.4). It is an easy exercise to show that in sums of games,games can be replaced by equivalent games,resulting in an equivalent sum.That is,for all games G,H,J,G≡H=⇒G+J≡H+J.(2.5)Note that(2.5)is not merely a re-statement of definition2.4,because equivalence of the games G+J and H+J means more than just that the games are either both winning or both losing(see the comments before lemma2.9below).Lemma2.6(The copycat principle)G+G≡∗0for any impartial game G. Proof.Given G,we assume by induction that the claim holds for all simpler games G .Any option of G+G is of the form G +G for an option G of G.This is winning by moving to the game G +G which is losing,by inductive assumption.So G+G is indeed a losing game,and therefore equivalent to∗0by lemma2.5.We now come back to the issue of inverse elements in abstract groups,mentioned at the end of section2.9.If we identify equivalent games,then the addition+of games defines indeed a group operation.The neutral element is∗0,or any equivalent game (that is,a losing game).The inverse of a game G,written as the negative−G,fulfilsG+(−G)≡∗0.(2.6) Lemma2.6shows that for an impartial game,−G is simply G itself.Side remark:For games that are not impartial,that is,partisan games,−G exists also.It is G but with the roles of the two players exchanged,so that whatever move was available to player I is now available to player II and vice versa.As an example, consider the game checkers(with the rule that whoever can no longer make a move loses),and let G be a certain configuration of pieces on the checkerboard.Then−G is the same configuration with the white and black pieces interchanged.Then in the game G+(−G),player II(who can move the black pieces,say),can also play‘copycat’.Namely,if player I makes a move in either G or−G with a white piece, then player II copies that move with a black piece on the other board(−G or G, respectively).Consequently,player II always has a move available and will win the game,so that G+(−G)is indeed a losing game for the starting player I,that is,G+(−G)≡∗0.However,we only consider impartial games,where−G=G.The following condition is very useful to prove that two games are equivalent. Lemma2.7Two impartial games G,H are equivalent if and only if G+H≡∗0.Proof.If G≡H,then by(2.5)and lemma2.6,G+H≡H+H≡∗0.Conversely,G+H≡∗0implies G≡G+H+H≡∗0+H≡H.Sometimes,we want to prove equivalence inductively,where the following observation is useful.Lemma2.8Two games G and H are equivalent if all their options are equivalent, that is,for every option of G there is an equivalent option of H and vice versa.。
游戏机制红宝书
SCVNGR游戏机制红宝书1.成就(Achievement)定义:完成某些任务之后获得的虚拟或实物回报。
示例:勋章、升级、奖励、积分。
2.动态约定(Appointment Dynamic)定义:为了取得成功,玩家必须在某个预定时间回到游戏完成某些动作。
通常与内部奖励计划和动态规避(avoidance dyanmics)相关。
示例: Cafe World和Farmville,如果你在某个预定时间回到游戏,可以获得某些奖励,否则你会失去某些东西。
3.规避(Avoidance)定义:并非通过奖励吸引玩家,而是避免某些损失,从而保持玩家活跃度。
示例:每隔30秒压一下杠杆,否则你会受到惊吓。
4.行为对比(Behavioral Contrast)定义:预期的改变会带来行为的急剧改变。
示例:猴子压一下杠杆可以得到一些生菜,高兴的它会继续压杠杆。
接着喂它一次葡萄,它很高兴。
下次猴子压杠杆还给它生菜,这次它会生气,把生菜扔给实验者。
5.行为惯性(Behavioral Momentum)定义:习惯成自然。
示例:如果玩家觉得有用或有趣,会一直玩下去。
6.越玩儿越带劲儿(Blissful Productivity)定义:比起放松,玩家在游戏中更愿意卖力。
示例:魔兽世界玩家通常每周玩22个小时,而且都是在下班后。
他们愿意努力游戏,比现实生活中还带劲儿,因为他们可以在游戏世界里找到工作的快感。
7.渐进式信息理论(Cascading Information Theory)定义:每一次只告诉玩家一点点内容,让玩家每到一个阶段获得相应的信息。
示例:首先展示一些基本动作,随着升级揭示更多动作。
8.链式计划(Chain Schedules)定义:把奖励和一系列附加事件联系到一起。
玩家会以为这些事件彼此独立。
玩家会把附加事件中完成的每个步骤看做是个人奖励。
示例:杀死10个兽人进入龙之洞穴,龙每隔30分钟出现一次。
9.共同发现(Communal Discovery)定义:整个社区联合起来解开某个谜题,或完成某项任务。
【汇编】2023年英语口语自我介绍15篇
2023年英语口语自我介绍15篇英语口语自我介绍1(1164字)译文:我的名字是____。
我毕业于____高中和主要在____。
有人在我的家人____。
我的父亲在一家计算机公司工作。
我妈妈是个家庭妇女。
我是家中最小的。
在我的`业余时间,我喜欢读小说。
我认为阅读可以扩展我的知识。
至于小说,我能想象我喜欢作为一个知名的科学家或功夫大师。
除了读书,我也喜欢玩电脑游戏。
许多成年人认为玩电脑游戏妨碍学习的学生。
但是我认为电脑游戏可以激励我学习一些东西,例如英语或日语。
我最喜欢的课程是英语,因为我觉得有趣的是,说的是一套通过不同的声音。
我希望我的英语可以提高在未来四年,能讲一口流利的英语在未来。
英文:My name is ____. I graduated from ____ senior high school and major in ____. There are ____ people in my family. My father works in a computer company. And my mother is a housewife. I am the youngest one in my family.In my spare time, I like to read novels. I think reading could enlarge my knowledge. As for novels, I could imaginewhatever I like such as a well-known scientist or a kung-fu master. In addition to reading, I also like to play PC games.A lot of grownups think playing PC games hinders the students from learning. But I think PC games could motivate me to learn something such as English or Japanese.My favorite course is English because I think it is interesting to say one thing via different sounds. I wish my English could be improved in the next four years and be able to speak fluent English in the future.英语口语自我介绍2(704字)Hello everyone.My name is .... I am a student of Grade eight . I am an outgoing , lovely girl and I am so welcomed by my friends and my classmates.I have a best friend, xiao hai. She is very interesting and lovely too. She often tells funny stories and always make me laugh. We often play together. I like action movies. I think they are exciting and interesting. I often go to the movies with my friends on weekends. I can aslo play the violin and have won many prizes in the competitions. I take violin lessons twicea week. It is a little hard for me but I am very happy , because I have a dream. I want to be a great violinist one day.Thank you.英语口语自我介绍3(780字)My name is __x. There are 4 people in my family. My father is a Chemistry teacher. He teaches chemistry in senior high school. My mother is an English teacher. She teaches English in the university. I have a younger brother, he is a junior high school student and is preparing for the entrance exam.I like to read English story books in my free time. Sometimes I surf the Internet and download the E- books to read. Reading E- books is fun. In addition, it also enlarges my vocabulary words because of the advanced technology and the vivid animations.I hope to study both English and computer technology because I am interested in both of the subjects. Maybe one day I could combine both of them and apply to my research in the future.英语口语自我介绍4(816字)Good morning,my dear classmates and teacher.It's my great honor to introduce myself to you here.My name is __,I'm from No.X middle school.I'm a __-year-old boygirl.Generally eaking, I am a hard working student do the thing (that)I'm interested in.In my are time,I often read books.I like watching movies and listening to music when I'm free.I pride myself on my drawing.Well,I'm an easy-going person.But I'm also a lazy boy that I don't like doing some orts.So,I want to erase myself.Now we are face to face,I want to face myself to start of something new,I will show you something new to see.Well,I just want to share with you,and I'm looking forward to making friends with you.So,let's be good friends!In the end,thank you for giving me the chance to introduce myself.That's all,thank you!英语口语自我介绍5(1033字)My name is __X.And I'm 13 years old.My hobbies are singing,dancing anddrawing pictures.And I'm a sanguine person.My favourite stars are Shu Chang and JJ Lam.I like playing computer game and reading classicin the rest.I'm studying at Xiao Shi Zhong Xin school.My name is Liu yang. I`m a thirteen-year-old boy. I`m a student of Class Four, Grade Seven in the No.3 middle school from Luo yang . My study is well. I`m tall and strong.I`m a diligent boy. I study hard at all my lessons. My dream is to be a knowledgeable man. I know it`s very hard. The way is very long. But I will try my best to do it well. And I belive myself. I have many hobies. They are reading, running, swimming, playing, basketball, climbing and playing table tennis. Everyday I all need some time to reading. On Saturdays I play basketball or play table tennis with my friends.In my free time, sometimes I help my mother with housework or do what I want to do. I have a colorful life!英语口语自我介绍6(791字)Good afternoon .I am of great hornor to stand here and introduce myself to you .First of all ,my english name is __X and my chinese name is __ If you are going to have a job interview ,you must say much things which can show your willness to this job ,such as ,it is my long cherished dreamto be ...and I am eager to get an opportunity to do...and then give some examples which can give evidence to .then you can say something about your hobbies .and it is best that the hobbies have something to do with the job.What is more important is do not forget to municate with the interviewee,keeping a smile and keeping your talks interesting and funny can contribute to the success.I hope you will give them a wonderful speech .Good luck to you !英语口语自我介绍7(512字)my name is __x. i am an outgoing girloy. i have many hobbies, such as basketball, football, reading and so on. among them i like reading best because when i am reading, i am very happy. reading helps me to know things that i do not know before. in my family there are four other members, my grandfather, grandmother, father and mother. my parents are strict with me while my grandparents are kind to me. i love them all and i want to study hard in return for their kindness to me.英语口语自我介绍8(4424字)I am a third year master major in automation at Shanghai Jiao Tong University, P. R. Ch ina. With tremendous interest in Industrial Engineering, I am writing to apply for acceptan ce into your Ph.D. graduate program. Education background In 1995, I entered the Nanjing University of Science & Technology (NUST) -- widely co nsidered one of the China’s best engineering schools. During the following undergraduate study, my academic records kept distinguished among the whole department. I was granted First Class Prize every semester, and my overall GPA(89.5/100) ranked No.1 among 113 students.In 1999, I got the privilege to enter the graduate program waived of the admiss ion test. I selected the Shanghai Jiao Tong University to continue my study for its best re putation on Combinatorial Optimization and Network Scheduling where my research interes t lies. At the period of my graduate study, my overall GPA(3.77/4.0) ranked top 5% in the depa rtment. In the second semester, I became teacher assistant that is given to talented and m atured students only. This year, I won the Acer Scholarship as the one and only candidate in my department, which is the ultimate accolade for distinguished students endowed by my university. Presently, I am preparing my graduation thesis and trying for the honor of ExcellentGraduation Thesis. Research experience and academic activity When a sophomore, I joined the Association of AI Enthusiast and began to narrow down my interest for my future research. In 1997, I participated in simulation tool development for the scheduling system in Prof. Wang’s lab. With the tool of OpenGL and Matlab, I d esigned a simulation program for transportation scheduling system. It is now widely used by different research groups in NUST. In 1998, I assumed and fulfilled a sewage analysis & dispose project for Nanjing sewage treatment plant. This was my first practice to conv ert a laboratory idea to a commercial product. In 1999, I joined the distinguished Professor Yu-Geng Xis research group aiming at Netw ork flow problem solving and Heuristic algorithm research. Soon I was engaged in the Fu Dan Gene Database Design. My duty was to pick up the useful information among differe nt kinds of gene matching format. Through the comparison and analysis for many heuristi c algorithms, I introduced an improved evolutionary algorithm -- Multi-population GeneticAlgorithm. By dividing a whole population into several sub-populations, this improved algorithm can effectively prevent GA from local convergence and promote various evolutionary orientations. It proved more efficiently than SGAin experiments, too. In the second semester, I joined the workshop-scheduling research in Shanghai Heavy Duty Tyre plant. The scheduling was designed for the rubber-making process that covered not only discrete but also continuous circumstances. To make a balance point between optimization quality and time cost, I proposed a Dynamic Layered Scheduling method based on hybrid Petri Nets. The practical application showed that the average makespan was shortened by a large scale. I also publicized two papers in core journals with this idea. Recently, I am doing research in the Composite Predict of the Electrical Power system assisted with the technology of Data Mining for Bao Steel. I try to combine the Decision Tree with Receding Optimization to provide a new solution for the Composite Predictive Problem. This project is now under construction.Besides, In July 20_, I got the opportunity to give a lecture in English in Asia Control Conference (ASCC) which is one of the top-level conferences among the world in the area of control and automation. In my senior year, I met Prof. Xiao-Song Lin, a visiting professor of mathematics from University of California-Riverside, I learned graph theory from him for my network research. These experiences all rapidlyexpanded my knowledge of English and the understanding of western culture.I hope to study in depthIn retrospect, I find myself standing on a solid basis in both theory and experience, which has prepared me for the Ph.D. program. My future research interests include: Network Scheduling Problem, Heuristic Algorithm research (especially in GA and Neural network), Supply chain network research, Hybrid system performance analysis with Petri nets and Data Mining.英语口语自我介绍9(606字)Hello everyone, my name is __, is a primary school student in grade two. My skin is white, has a pair of bright eyes round face.I am a boy loves a lot, such as drawing, practicing martial arts, calligraphy I have love, but I like best is running, every class I and his classmates Lv __ running.I was a greedy boy, see good, I quickly ran home, mom to give me pocket money.I was a little boy with a careless, I am happy to have a test in the"fortune" hard "Xin", got 99 points, the next time I must not be so careless.This is me, a greedy, careless and lovely boy.英语口语自我介绍10(350字)Hello.My English name is Amy.Im from China.Im thirteen..Now,Im in chinaNo.1 juior High School.Im in Class One,Grade One.My photo number 62965754.I have a friend.Her name is Ann .Shi is twelve years old.Shi is in Class Three,Grade One.She photo number is 65225233.We are not in the same class.But we are good friends.英语口语自我介绍11(989字)Hello, everyone,From a middle class family, I was born in Hsin Ying, Tainan on October 10th, 1965. My father is a civil official at Tainan City Government. My mother is a house wife good at cooking. Although I am the only child of my parents, I am by no mans a spoiled one. On the contrary, I have been expected to be a successful man with advanced education. I studyhard at school. Besides texts knowledge, journalism is my favorite; whenever reading, my heart is filled with great joy and interesting.“Being good is must; successful, however, is plus.” Father adopts the idea of his father.Especially in military service, I realized it more precisely. People said: Military service makes a boy to man, I agree that.I realized the importance of English and began to study diligently when I was eighteen. I did not start in my early age, but I hope that I could pass the test of General English Proficiency Test. And this is my best wish at the moment.英语口语自我介绍12(524字)Good morning. I am glad to be here for this interview. I’m __ and I have been working as a __ at __ for ... years since my graduation from __ University. During my undergraduate years, I developed an interest in __. Then I took several selective courses on __ and read a lot of literature in this field. However, it is one thing to study __ theory in university, but another to put theory into practice. That’s why I decide to be a __ to enrich both my __ knowledge and practical eperiences.!英语口语自我介绍13(1703字)Hello, everyone! My name is ---, you can also call me ---.I study at No.-- middle school now. I’m not outgoing. I like reading books alone. I am also fond of listening to music. I love country music. I like watching cartoons, too. Because of Japanese cartoons, I can speak a little Japanese. Maybe I’m crazy!As for subjects, I think I can learn well in many subjects.I like English best. Because it’s interesting. And I think it’s a good way to communicate with foreigners. It’s very useful.I am weak in P.E. I like P.E but I can’t do it well. But I e-ercise every day morning from now on. I believe I can do it well one day.I’m the monitress of our class now. I will try to work hard for teachers and classmates. A lot of people said I was a good child. But I will try my best to correct my disadvantages. I'm trying to be a perfect girl.Thanks for listening.goodafternoon, teachers! today, i——m very happy to make a speech here. now, let me introduce myself. my name is zhuruijie.my english name is molly. i——m 12.i come from class1 grade 6 of tongpu no.2 primary school. i——m an active girl. i like playing basketball.because i think it——s very interesting. i——d like to eat potatoes. they——re tasty.my favourite colour is green. and i like math best. it ——s fun. on the weekend, i like reading books in my room. i have a happy family.my father is tall and strong.my mother is hard-working and tall, too. my brother is smart. and i——m a good student. my dream is to be a teacher, because i want to help the poor children in the future.thank you for listening! please remember me!英语口语自我介绍14(456字)Hello,my name is __x.I am12 years old.__ grade student.I came from__x.There were __x members inside my family such as father,mother and me.My favorite thing to do is watch TV and sleep.There were lots of the things about myself but I don't know what to tell right now.Don't worry!You will know more thing about myself later.Today is the first day that I came to this class.I hope we will have a good time in the class.Thanks You!英语口语自我介绍15(4076字)I called _x, male, (1990 -), _x people, studying in _x professional.20_ I am ready to apply for _x Design Studios students. We will make personal evaluation on individual moral cultivation, academic motivation, academic background, knowledge reserve, study plan and employment target. Please teacher review and inspection!Moral cultivationMoral cultivation I thought correct, warm and sincere, willing to help within the context of the people around. Good cheerful people, honest, enthusiastic, positive attitude towards life, willing to participate in various public welfare activities, a strong sense of social responsibility and justice.Respect teachers, unity of students, the work of enthusiasm, hard-working, strong sense of responsibility.Academic motivationSpeaking of academic motivation, in fact, very simple, you can answer from two aspects: for himself and for him. "For" or can be said to be the heart of one's own, this "heart" is the private heart of the academic ideals and the future of academic life. "A pen, a book, a cup of tea," the academic life for me has endless appeal to guide my constant to the academic door of the forward. "For him" or can also be used in a sentence can make a clear statement: "academic is the world of the public device." As a member of society, as a member of the Communist Party, the development of society is not a matter of their own nature, if what I can contribute to our country and society, the academic is the biggest weapon I long forAcademic background and knowledge reserveAs a master of design graduate, the design research is still only in the embryonic stage. One and a half years of master's study, I work hard, in addition to reading a large number of articles within the professional, but also actively involved in the subject of the instructor, mastered a more completeseries of design methods. These, for my future study of the road to lay a more solid foundation. During the graduate school, I completed the master's course with good results. I am good at the theory of learning and academic practice, the completion of the mentor layout of the project.research workDuring the graduate school, he assisted the tutor to apply for the international science and technology support program _x declaration work, this subject has been approved and successful in the study.Many times as a mentor of thecross-topic leader in a number of research projects such as: _x, _x, _x, good completion of the task of the mentor layout.study planWork hard to learn and improve the professional theoretical knowledge, and strive to learn more about the forefront of subjects and development direction, at the same time under the humanities history, organization and management knowledge, and strive to have expertise in a wide range of knowledge complex talents; combined mentor research projects And engineering projects, mastered the methods of scientific research, training in scientific papers writing ability, andprofessional knowledge and production practice, learning in practice.Employment goalsFor their own employment goals, has always been very clear: into the university or research institutions to do the work of teaching and research. Now is the case, the future will think so. Because the analysis of personal traits, I think universities and research institutions is a more suitable for my environment, and reading books is also a big hobby.The above is my personal statement, while writing a pen to write on Kant tombstone on a famous saying: "There are two things I think the more I feel more magical, the hearts of the more full of awe, that is my head and my heart Of the moral standards. "For the sky looking up, is my academic progress, the pursuit of truth great motivation, I also learn to remember the law, for the inner moral law to comply with, is my life in the future for the people and the road Strictly abide by the moral line of doing things.。
英语第一章阅读 game theory 原文及翻译
The essence of a game is the interdependence of player strategies. There are two distinct types of strategic interdependence: sequential and simultaneous. In the former the players move in sequence, each aware of the others’ previous actions. In the latter the players act at the same time, each ignorant of the others’ actions.
Game theory was pioneered by Princeton mathematician john von Neumann. In the early years the emphasis was on games of pure conflict (zero-sum games). Other games were considered in a cooperative form. That is, the participants were supposed to choose and implement their actions jointly. Recent research has focused on games that are neither zero sum nor purely cooperative. In these games the players choose their actions separately, but their links to others involve elements of both competition and cooperation.
game theory 教材
Game Theory 教材一、介绍Game Theory是一种研究决策问题的数学理论,它关注的是理性行为体在面临复杂互动环境时的选择和行动。
Game Theory可以广泛应用于经济学、政治学、社会学等领域,帮助人们理解和解释现实世界的各种互动现象。
本教材旨在介绍Game Theory的基本概念、方法和应用,为读者提供一种理解和分析现实世界中复杂问题的工具。
二、内容第一章:Game Theory概述本章将介绍Game Theory的基本概念、发展历程和应用领域。
我们将探讨理性行为体的假设、互动决策的基本模式以及Game Theory 的主要研究问题。
第二章:策略博弈本章将介绍策略博弈的基本概念和方法,包括策略博弈的定义、纳什均衡、零和博弈和囚徒困境等。
我们将通过实例和分析来理解和应用这些概念和方法。
第三章:非策略博弈本章将介绍非策略博弈的基本概念和方法,包括非策略博弈的定义、优势策略和劣势策略、不完全信息博弈和拍卖理论等。
我们将通过实例和分析来理解和应用这些概念和方法。
第四章:演化博弈本章将介绍演化博弈的基本概念和方法,包括演化博弈的定义、演化稳定性和动态演化博弈等。
我们将通过实例和分析来理解和应用这些概念和方法。
第五章:应用案例本章将介绍Game Theory在经济学、政治学和社会学等领域的应用案例,包括市场交易、政治选举和社会规范等。
我们将通过案例分析和讨论来深入理解和应用Game Theory的概念和方法。
三、结论本教材旨在介绍Game Theory的基本概念、方法和应用,帮助读者理解和分析现实世界中各种复杂的互动现象。
通过阅读和实践,读者可以更好地理解和掌握Game Theory,并应用于解决现实问题中。
博弈论-game-theory-两人轮流进行游戏
当k∞时 x 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 …… g(x) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 …… 这有啥用
游戏的联合
定义:对于n个给定的公平组合博弈G1, G2, …, Gn,定义他 们集的合联 ;合对为于G一=个G1局+G面2+x…i属+G于n.X对i,于设游F戏i(xGi)i表Байду номын сангаас示设xXi的i为后它继的局局面面集 合对。于G那的么一G个的局局面面x集=合{x1X,x=2,X…1*,xXn2}*,…它*X的n(后其继中局*为面笛集卡合儿积);
gn(x1,x2,…,xn) = g(x1)⊕g(x2)⊕…⊕g(xn)
= x1⊕x2⊕…⊕xn
经典Nim游戏
图的游戏
3
0
2 0
1
3 ⊕0 ⊕0=3
0 0
1 0
1
Anti-Nim
有n堆石子,每堆ai个,两个人轮流游戏,每次游戏者 取走某一石碓中至少1枚,至多k枚的石子。谁取走最 后一颗石子算谁输。
一方算输 无论游戏如何进行,总可以在有限步之内结束。(the
Ending Condition)
N局面,P局面
N局面——先手必胜局面
winning for the Next player
P局面——后手必胜局面
winning for the Previous player
定义:
每一个最终局面都是P局面 对于一个局面,若至少有一种操作使它变成一个P局面,
还扩展
游戏4:游戏有n堆石子,第i堆有ai枚,两人轮流进行 游戏,每次游戏者可以从任意一堆取走任意多枚石子, 也可以将任意的一堆石子任意的分成两堆。谁取走最 后一颗石子为胜。
Mathematical Introduction to Game Theory1
GAME THEORYThomas S.FergusonUniversity of California at Los AngelesINTRODUCTION.Game theory is a fascinating subject.We all know many entertaining games,such as chess,poker,tic-tac-toe,bridge,baseball,computer games—the list is quite varied and almost endless.In addition,there is a vast area of economic games,discussed in Myerson(1991)and Kreps(1990),and the related political games,Ordeshook(1986), Shubik(1982),and Taylor(1995).The competition betweenfirms,the conflict between management and labor,thefight to get bills through congress,the power of the judiciary, war and peace negotiations between countries,and so on,all provide examples of games in action.There are also psychological games played on a personal level,where the weapons are words,and the payoffs are good or bad feelings,Berne(1964).There are biological games,the competition between species,where natural selection can be modeled as a game played between genes,Smith(1982).There is a connection between game theory and the mathematical areas of logic and computer science.One may view theoretical statistics as a two person game in which nature takes the role of one of the players,as in Blackwell and Girshick(1954)and Ferguson(1968).Games are characterized by a number of players or decision makers who interact, possibly threaten each other and form coalitions,take actions under uncertain conditions, andfinally receive some benefit or reward or possibly some punishment or monetary loss. In this text,we present various mathematical models of games and study the phenomena that arise.In some cases,we will be able to suggest what courses of action should be taken by the players.In others,we hope simply to be able to understand what is happening in order to make better predictions about the future.As we outline the contents of this text,we introduce some of the key words and terminology used in game theory.First there is the number of players which will be denoted by n.Let us label the players with the integers1to n,and denote the set of players by N={1,2,...,n}.We study mostly two person games,n=2,where the concepts are clearer and the conclusions are more definite.When specialized to one-player, the theory is simply called decision theory.Games of solitaire and puzzles are examples of one-person games as are various sequential optimization problems found in operations research,and optimization,(see Papadimitriou and Steiglitz(1982)for example),or linear programming,(see Chv´a tal(1983)),or gambling(see Dubins and Savage(1965)).There are even things called“zero-person games”,such as the“game of life”of Conway(seeBerlekamp et al.(1982)Chap.25);once an automaton gets set in motion,it keeps going without any person making decisions.We assume throughout that there are at least two players,that is,n≥2.In macroeconomic models,the number of players can be very large, ranging into the millions.In such models it is often preferable to assume that there are an infinite number of players.In fact it has been found useful in many situations to assume there are a continuum of players,with each player having an infinitesimal influence on the outcome as in Aumann and Shapley(1974).(Incidentally,both authors were later to win Nobel Prizes in Economics.)In this course,we take n to befinite.There are three main mathematical models or forms used in the study of games,the extensive form,the strategic form and the coalitional form.These differ in the amount of detail on the play of the game built into the model.The most detail is given in the extensive form,where the structure closely follows the actual rules of the game.In the extensive form of a game,we are able to speak of a position in the game,and of a move of the game as moving from one position to another.The set of possible moves from a position may depend on the player whose turn it is to move from that position. In the extensive form of a game,some of the moves may be random moves,such as the dealing of cards or the rolling of dice.The rules of the game specify the probabilities of the outcomes of the random moves.One may also speak of the information players have when they move.Do they know all past moves in the game by the other players?Do they know the outcomes of the random moves?When the players know all past moves by all the players and the outcomes of all past random moves,the game is said to be of perfect information.Two-person games of perfect information with win or lose outcome and no chance moves are known as combi-natorial games.There is a beautiful and deep mathematical theory of such games.You mayfind an exposition of it in Conway(1976)and in Berlekamp et al.(1982).Such a game is said to be impartial if the two players have the same set of legal moves from each position,and it is said to be partizan otherwise.Part I of this text contains an introduc-tion to the theory of impartial combinatorial games.For another elementary treatment of impartial games see the book by Guy(1989).We begin Part II by describing the strategic form or normal form of a game.In the strategic form,many of the details of the game such as position and move are lost;the main concepts are those of a strategy and a payoff.In the strategic form,each player chooses a strategy from a set of possible strategies.We denote the strategy set or action space of player i by A i,for i=1,2,...,n.Each player considers all the other players and their possible strategies,and then chooses a specific strategy from his strategy set.All players make such a choice simultaneously,the choices are revealed and the game ends with each player receiving some payoff.Each player’s choice may influence thefinal outcome for all the players.We model the payoffs as taking on numerical values.In general the payoffs may be quite complex entities,such as“you receive a ticket to a baseball game tomorrow when there is a good chance of rain,and your raincoat is torn”.The mathematical and philosophical justification behind the assumption that each player can replace such payoffs with numerical values is discussed in the Appendix under the title,Utility Theory.Thistheory is treated in detail in the books of Savage(1954)and of Fishburn(1988).We therefore assume that each player receives a numerical payoffthat depends on the actions chosen by all the players.Suppose player1chooses a1∈A1,player2chooses a2∈A2,etc. and player n chooses a n∈A n.Then we denote the payoffto player j,for j=1,2,...,n,by f j(a1,a2,...,a n),and call it the payofffunction for player j.The strategic form of a game is defined then by the three objects:(1)the set,N={1,2,...,n},of players,(2)the sequence,A1,...,A n,of strategy sets of the players,and(3)the sequence,f1(a1,...,a n),...,f n(a1,...,a n),of real-valued payofffunctions of the players.A game in strategic form is said to be zero-sum if the sum of the payoffs to the players is zero no matter what actions are chosen by the players.That is,the game is zero-sum ifnf i(a1,a2,...,a n)=0i=1for all a1∈A1,a2∈A2,...,a n∈A n.In thefirst four chapters of Part II,we restrict attention to the strategic form offinite,two-person,zero-sum games.Such a game is said to befinite if both the strategy sets arefinite sets.Theoretically,such games have clear-cut solutions,thanks to a fundamental mathematical result known as the minimax theorem.Each such game has a value,and both players have optimal strategies that guarantee the value.In the last three chapters of Part II,we treat two-person zero-sum games in extensive form,and show the connection between the strategic and extensive forms of games.In particular,one of the methods of solving extensive form games is to solve the equivalent strategic form.Here,we give an introduction to Recursive Games and Stochastic Games, an area of intense contemporary development(see Filar and Vrieze(1997),Maitra and Sudderth(1996)and Sorin(2002)).In the last chapter,we investigate the problems that arise when at least one of the strategy sets of the players is an infinite set.In Part III,the theory is extended to two-person non-zero-sum games.Here the situation is more nebulous.In general,such games do not have values and players do not have optimal strategies.The theory breaks naturally into two parts.There is the noncooperative theory in which the players,if they may communicate,may not form binding agreements.This is the area of most interest to economists,see Gibbons(1992), and Bierman and Fernandez(1993),for example.In1994,John Nash,John Harsanyi and Reinhard Selten received the Nobel Prize in Economics for work in this area.Such a theory is natural in negotiations between nations when there is no overseeing body to enforce agreements,and in business dealings where companies are forbidden to enter into agreements by laws concerning constraint of trade.The main concept,replacing value and optimal strategy is the notion of a strategic equilibrium,also called a Nash equilibrium.This theory is treated in thefirst three chapters of Part III.On the other hand,in the cooperative theory the players are allowed to form binding agreements,and so there is strong incentive to work together to receive the largest total payoff.The problem then is how to split the total payoffbetween or among the players. This theory also splits into two parts.If the players measure utility of the payoffin the same units and there is a means of exchange of utility such as side payments,we say the game has transferable utility;otherwise non-transferable utility.The last chapter of Part III treat these topics.When the number of players grows large,even the strategic form of a game,though less detailed than the extensive form,becomes too complex for analysis.In the coalitional form of a game,the notion of a strategy disappears;the main features are those of a coalition and the value or worth of the coalition.In many-player games,there is a tendency for the players to form coalitions to favor common interests.It is assumed each coalition can guarantee its members a certain amount,called the value of the coalition. The coalitional form of a game is a part of cooperative game theory with transferable utility,so it is natural to assume that the grand coalition,consisting of all the players, will form,and it is a question of how the payoffreceived by the grand coalition should be shared among the players.We will treat the coalitional form of games in Part IV.There we introduce the important concepts of the core of an economy.The core is a set of payoffs to the players where each coalition receives at least its value.An important example is two-sided matching treated in Roth and Sotomayor(1990).We will also look for principles that lead to a unique way to split the payofffrom the grand coalition,such as the Shapley value and the nucleolus.This will allow us to speak of the power of various members of legislatures.We will also examine cost allocation problems(how should the cost of a project be shared by persons who benefit unequally from it).Related Texts.There are many texts at the undergraduate level that treat various aspects of game theory.Accessible texts that cover certain of the topics treated in this text are the books of Straffin(1993),Morris(1994)and Tijs(2003).The book of Owen (1982)is another undergraduate text,at a slightly more advanced mathematical level.The economics perspective is presented in the entertaining book of Binmore(1992).The New Palmgrave book on game theory,Eatwell et al.(1987),contains a collection of historical sketches,essays and expositions on a wide variety of topics.Older texts by Luce and Raiffa(1957)and Karlin(1959)were of such high quality and success that they have been reprinted in inexpensive Dover Publications editions.The elementary and enjoyable book by Williams(1966)treats the two-person zero-sum part of the theory.Also recommended are the lectures on game theory by Robert Aumann(1989),one of the leading scholars of thefield.And last,but actuallyfirst,there is the book by von Neumann and Morgenstern (1944),that started the wholefield of game theory.References.Robert J.Aumann(1989)Lectures on Game Theory,Westview Press,Inc.,Boulder,Col-orado.R.J.Aumann and L.S.Shapley(1974)Values of Non-atomic Games,Princeton University Press.E.R.Berlekamp,J.H.Conway and R.K.Guy(1982),Winning Ways for your Mathe-matical Plays(two volumes),Academic Press,London.Eric Berne(1964)Games People Play,Grove Press Inc.,New York.H.Scott Bierman and Luis Fernandez(1993)Game Theory with Economic Applications,2nd ed.(1998),Addison-Wesley Publishing Co.Ken Binmore(1992)Fun and Games—A Text on Game Theory,D.C.Heath,Lexington, Mass.D.Blackwell and M.A.Girshick(1954)Theory of Games and Statistical Decisions,JohnWiley&Sons,New York.V.Chv´a tal(1983)Linear Programming,W.H.Freeman,New York.J.H.Conway(1976)On Numbers and Games,Academic Press,New York.Lester E.Dubins amd Leonard J.Savage(1965)How to Gamble If You Must:Inequal-ities for Stochastic Processes,McGraw-Hill,New York.2nd edition(1976)Dover Publications Inc.,New York.J.Eatwell,gate and P.Newman,Eds.(1987)The New Palmgrave:Game Theory, W.W.Norton,New York.Thomas S.Ferguson(1968)Mathematical Statistics–A Decision-Theoretic Approach, Academic Press,New York.J.Filar and K.Vrieze(1997)Competitive Markov Decision Processes,Springer-Verlag, New York.Peter C.Fishburn(1988)Nonlinear Preference and Utility Theory,John Hopkins Univer-sity Press,Baltimore.Robert Gibbons(1992)Game Theory for Applied Economists,Princeton University Press. Richard K.Guy(1989)Fair Game,COMAP Mathematical Exploration Series.Samuel Karlin(1959)Mathematical Methods and Theory in Games,Programming and Economics,in two vols.,Reprinted1992,Dover Publications Inc.,New York. David M.Kreps(1990)Game Theory and Economic Modeling,Oxford University Press. R.D.Luce and H.Raiffa(1957)Games and Decisions—Introduction and Critical Survey, reprinted1989,Dover Publications Inc.,New York.A.P.Maitra ans W.D.Sudderth(1996)Discrete Gambling and Stochastic Games,Ap-plications of Mathematics32,Springer.Peter Morris(1994)Introduction to Game Theory,Springer-Verlag,New York.Roger B.Myerson(1991)Game Theory—Analysis of Conflict,Harvard University Press. Peter C.Ordeshook(1986)Game Theory and Political Theory,Cambridge University Press.Guillermo Owen(1982)Game Theory2nd Edition,Academic Press.Christos H.Papadimitriou and Kenneth Steiglitz(1982)Combinatorial Optimization,re-printed(1998),Dover Publications Inc.,New York.Alvin E.Roth and Marilda A.Oliveira Sotomayor(1990)Two-Sided Matching–A Study in Game-Theoretic Modeling and Analysis,Cambridge University Press.L.J.Savage(1954)The Foundations of Statistics,John Wiley&Sons,New York. Martin Shubik(1982)Game Theory in the Social Sciences,The MIT Press.John Maynard Smith(1982)Evolution and the Theory of Games,Cambridge University Press.Sylvain Sorin(2002)A First Course on Zero-Sum Repeated Games,Math´e matiques& Applications37,Springer.Philip D.Straffin(1993)Game Theory and Strategy,Mathematical Association of Amer-ica.Alan D.Taylor(1995)Mathematics and Politics—Strategy,Voting,Power and Proof, Springer-Verlag,New York.Stef Tijs(2003)Introduction to Game Theory,Hindustan Book Agency,India.J.von Neumann and O.Morgenstern(1944)The Theory of Games and Economic Behavior, Princeton University Press.John D.Williams,(1966)The Compleat Strategyst,2nd Edition,McGraw-Hill,New York.。
自动化英语专业英语词汇表
自动化英语专业英语词汇表文章摘要:本文介绍了自动化英语专业的一些常用的英语词汇,包括自动化技术、控制理论、系统工程、人工智能、模糊逻辑等方面的专业术语。
本文按照字母顺序,将这些词汇分为26个表格,每个表格包含了以相应字母开头的词汇及其中文释义。
本文旨在帮助自动化专业的学习者和从业者掌握和使用这些专业英语词汇,提高他们的英语水平和专业素养。
A英文中文acceleration transducer加速度传感器acceptance testing验收测试accessibility可及性accumulated error累积误差AC-DC-AC frequency converter交-直-交变频器AC (alternating current) electric drive交流电子传动active attitude stabilization主动姿态稳定actuator驱动器,执行机构adaline线性适应元adaptation layer适应层adaptive telemeter system适应遥测系统adjoint operator伴随算子admissible error容许误差aggregation matrix集结矩阵AHP (analytic hierarchy process)层次分析法amplifying element放大环节analog-digital conversion模数转换annunciator信号器antenna pointing control天线指向控制anti-integral windup抗积分饱卷aperiodic decomposition非周期分解a posteriori estimate后验估计approximate reasoning近似推理a priori estimate先验估计articulated robot关节型机器人assignment problem配置问题,分配问题associative memory model联想记忆模型associatron联想机asymptotic stability渐进稳定性attained pose drift实际位姿漂移B英文中文attitude acquisition姿态捕获AOCS (attritude and orbit control system)姿态轨道控制系统attitude angular velocity姿态角速度attitude disturbance姿态扰动attitude maneuver姿态机动attractor吸引子augment ability可扩充性augmented system增广系统automatic manual station自动-手动操作器automaton自动机autonomous system自治系统backlash characteristics间隙特性base coordinate system基座坐标系Bayes classifier贝叶斯分类器bearing alignment方位对准bellows pressure gauge波纹管压力表benefit-cost analysis收益成本分析bilinear system双线性系统biocybernetics生物控制论biological feedback system生物反馈系统C英文中文calibration校准,定标canonical form标准形式canonical realization标准实现capacity coefficient容量系数cascade control级联控制causal system因果系统cell单元,元胞cellular automaton元胞自动机central processing unit (CPU)中央处理器certainty factor确信因子characteristic equation特征方程characteristic function特征函数characteristic polynomial特征多项式characteristic root特征根英文中文charge-coupled device (CCD)电荷耦合器件chaotic system混沌系统check valve单向阀,止回阀chattering phenomenon颤振现象closed-loop control system闭环控制系统closed-loop gain闭环增益cluster analysis聚类分析coefficient of variation变异系数cogging torque齿槽转矩,卡齿转矩cognitive map认知图,认知地图coherency matrix相干矩阵collocation method配点法,配置法combinatorial optimization problem组合优化问题common mode rejection ratio (CMRR)共模抑制比,共模抑制率commutation circuit换相电路,换向电路commutator motor换向电动机D英文中文damping coefficient阻尼系数damping ratio阻尼比data acquisition system (DAS)数据采集系统data fusion数据融合dead zone死区decision analysis决策分析decision feedback equalizer (DFE)决策反馈均衡器decision making决策,决策制定decision support system (DSS)决策支持系统decision table决策表decision tree决策树decentralized control system分散控制系统decoupling control解耦控制defuzzification去模糊化,反模糊化delay element延时环节,滞后环节delta robot德尔塔机器人demodulation解调,检波density function密度函数,概率密度函数derivative action微分作用,微分动作design matrix设计矩阵E英文中文eigenvalue特征值,本征值eigenvector特征向量,本征向量elastic element弹性环节electric drive电子传动electric potential电势electro-hydraulic servo system电液伺服系统electro-mechanical coupling system电机耦合系统electro-pneumatic servo system电气伺服系统electronic governor电子调速器encoder编码器,编码装置end effector末端执行器,末端效应器entropy熵equivalent circuit等效电路error analysis误差分析error bound误差界,误差限error signal误差信号estimation theory估计理论Euclidean distance欧几里得距离,欧氏距离Euler angle欧拉角Euler equation欧拉方程F英文中文factor analysis因子分析factorization method因子法,因式分解法feedback反馈,反馈作用feedback control反馈控制feedback linearization反馈线性化feedforward前馈,前馈作用feedforward control前馈控制field effect transistor (FET)场效应晶体管filter滤波器,滤波环节finite automaton有限自动机finite difference method有限差分法finite element method (FEM)有限元法finite impulse response (FIR) filter有限冲激响应滤波器first-order system一阶系统fixed-point iteration method不动点迭代法flag register标志寄存器flip-flop circuit触发器电路floating-point number浮点数flow chart流程图,流程表fluid power system流体动力系统G英文中文gain增益gain margin增益裕度Galerkin method伽辽金法game theory博弈论Gauss elimination method高斯消元法Gauss-Jordan method高斯-约当法Gauss-Markov process高斯-马尔可夫过程Gauss-Seidel iteration method高斯-赛德尔迭代法genetic algorithm (GA)遗传算法gradient method梯度法,梯度下降法graph theory图论gravity gradient stabilization重力梯度稳定gray code格雷码,反向码gray level灰度,灰阶grid search method网格搜索法ground station地面站,地面控制站guidance system制导系统,导航系统gyroscope陀螺仪,陀螺仪器H英文中文H∞ control H无穷控制Hamiltonian function哈密顿函数harmonic analysis谐波分析harmonic oscillator谐振子,谐振环节Hartley transform哈特利变换Hebb learning rule赫布学习规则Heisenberg uncertainty principle海森堡不确定性原理hidden layer隐层,隐含层hidden Markov model (HMM)隐马尔可夫模型hierarchical control system分层控制系统high-pass filter高通滤波器Hilbert transform希尔伯特变换Hopfield network霍普菲尔德网络hysteresis滞后,迟滞,磁滞I英文中文identification识别,辨识identity matrix单位矩阵,恒等矩阵image processing图像处理impulse response冲激响应impulse response function冲激响应函数inadmissible control不可接受控制incremental encoder增量式编码器indefinite integral不定积分index of controllability可控性指标index of observability可观测性指标induction motor感应电动机inertial navigation system (INS)惯性导航系统inference engine推理引擎,推理机inference rule推理规则infinite impulse response (IIR) filter无限冲激响应滤波器information entropy信息熵information theory信息论input-output linearization输入输出线性化input-output model输入输出模型input-output stability输入输出稳定性J英文中文Jacobian matrix雅可比矩阵jerk加加速度,冲击joint coordinate system关节坐标系joint space关节空间Joule's law焦耳定律jump resonance跳跃共振K英文中文Kalman filter卡尔曼滤波器Karhunen-Loeve transform卡尔胡南-洛维变换kernel function核函数,核心函数kinematic chain运动链,运动链条kinematic equation运动方程,运动学方程kinematic pair运动副,运动对kinematics运动学kinetic energy动能L英文中文Lagrange equation拉格朗日方程Lagrange multiplier拉格朗日乘子Laplace transform拉普拉斯变换Laplacian operator拉普拉斯算子laser激光,激光器latent root潜根,隐根latent vector潜向量,隐向量learning rate学习率,学习速度least squares method最小二乘法Lebesgue integral勒贝格积分Legendre polynomial勒让德多项式Lennard-Jones potential莱纳德-琼斯势level set method水平集方法Liapunov equation李雅普诺夫方程Liapunov function李雅普诺夫函数Liapunov stability李雅普诺夫稳定性limit cycle极限环,极限圈linear programming线性规划linear quadratic regulator (LQR)线性二次型调节器linear system线性系统M英文中文machine learning机器学习machine vision机器视觉magnetic circuit磁路,磁电路英文中文magnetic flux磁通量magnetic levitation磁悬浮magnetization curve磁化曲线magnetoresistance磁阻,磁阻效应manipulability可操作性,可操纵性manipulator操纵器,机械手Markov chain马尔可夫链Markov decision process (MDP)马尔可夫决策过程Markov property马尔可夫性质mass matrix质量矩阵master-slave control system主从控制系统matrix inversion lemma矩阵求逆引理maximum likelihood estimation (MLE)最大似然估计mean square error (MSE)均方误差measurement noise测量噪声,观测噪声mechanical impedance机械阻抗membership function隶属函数N英文中文natural frequency固有频率,自然频率natural language processing (NLP)自然语言处理navigation导航,航行negative feedback负反馈,负反馈作用neural network神经网络neuron神经元,神经细胞Newton method牛顿法,牛顿迭代法Newton-Raphson method牛顿-拉夫逊法noise噪声,噪音nonlinear programming非线性规划nonlinear system非线性系统norm范数,模,标准normal distribution正态分布,高斯分布notch filter凹槽滤波器,陷波滤波器null space零空间,核空间O英文中文observability可观测性英文中文observer观测器,观察器optimal control最优控制optimal estimation最优估计optimal filter最优滤波器optimization优化,最优化orthogonal matrix正交矩阵oscillation振荡,振动output feedback输出反馈output regulation输出调节P英文中文parallel connection并联,并联连接parameter estimation参数估计parity bit奇偶校验位partial differential equation (PDE)偏微分方程passive attitude stabilization被动姿态稳定pattern recognition模式识别PD (proportional-derivative) control比例-微分控制peak value峰值,峰值幅度perceptron感知器,感知机performance index性能指标,性能函数period周期,周期时间periodic signal周期信号phase angle相角,相位角phase margin相位裕度phase plane analysis相平面分析phase portrait相轨迹,相图像PID (proportional-integral-derivative) control比例-积分-微分控制piezoelectric effect压电效应pitch angle俯仰角pixel像素,像元Q英文中文quadratic programming二次规划quantization量化,量子化quantum computer量子计算机quantum control量子控制英文中文queueing theory排队论quiescent point静态工作点,静止点R英文中文radial basis function (RBF) network径向基函数网络radiation pressure辐射压random variable随机变量random walk随机游走range范围,区间,距离rank秩,等级rate of change变化率,变化速率rational function有理函数Rayleigh quotient瑞利商real-time control system实时控制系统recursive algorithm递归算法recursive estimation递归估计reference input参考输入,期望输入reference model参考模型,期望模型reinforcement learning强化学习relay control system继电器控制系统reliability可靠性,可信度remote control system遥控系统,远程控制系统residual error残差误差,残余误差resonance frequency共振频率S英文中文sampling采样,取样sampling frequency采样频率sampling theorem采样定理saturation饱和,饱和度scalar product标量积,点积scaling factor缩放因子,比例系数Schmitt trigger施密特触发器Schur complement舒尔补second-order system二阶系统self-learning自学习,自我学习self-organizing map (SOM)自组织映射sensitivity灵敏度,敏感性sensitivity analysis灵敏度分析,敏感性分析sensor传感器,感应器sensor fusion传感器融合servo amplifier伺服放大器servo motor伺服电机,伺服马达servo valve伺服阀,伺服阀门set point设定值,给定值settling time定常时间,稳定时间T英文中文tabu search禁忌搜索,禁忌表搜索Taylor series泰勒级数,泰勒展开式teleoperation遥操作,远程操作temperature sensor温度传感器terminal终端,端子testability可测试性,可检测性thermal noise热噪声,热噪音thermocouple热电偶,热偶threshold阈值,门槛time constant时间常数time delay时延,延时time domain时域time-invariant system时不变系统time-optimal control时间最优控制time series analysis时间序列分析toggle switch拨动开关,切换开关tolerance analysis公差分析torque sensor扭矩传感器transfer function传递函数,迁移函数transient response瞬态响应U英文中文uncertainty不确定性,不确定度underdamped system欠阻尼系统undershoot低于量,低于值unit impulse function单位冲激函数unit step function单位阶跃函数unstable equilibrium point不稳定平衡点unsupervised learning无监督学习upper bound上界,上限utility function效用函数,效益函数V英文中文variable structure control变结构控制variance方差,变异vector product向量积,叉积velocity sensor速度传感器verification验证,校验virtual reality虚拟现实viscosity粘度,黏度vision sensor视觉传感器voltage电压,电位差voltage-controlled oscillator (VCO)电压控制振荡器W英文中文wavelet transform小波变换weighting function加权函数Wiener filter维纳滤波器Wiener process维纳过程work envelope工作空间,工作范围worst-case analysis最坏情况分析X英文中文XOR (exclusive OR) gate异或门,异或逻辑门Y英文中文yaw angle偏航角Z英文中文Z transform Z变换zero-order hold (ZOH)零阶保持器zero-order system零阶系统zero-pole cancellation零极点抵消。
用英语介绍自己的工作经验
用英语介绍自己的工作经验I: Please tell me your present job.A: I am working in a garment factory. My present job is to inspect the quality of products. comparatively speaking, quality control is rather simple. Although I do my job well, I am looking for a new job which is more challenging.I: Have you ever been employed?A: Not yet. I originally planned to go abroad to study after leaving college, but I couldn't get a visa.I: Your resume says you have had one experience working in a foreign representative office in Shanghai, may I ask why you left?A: I worked in a foreign rep. office for one year. However, I left there two years ago because the work they gave me was rather dull. I found another job which is more interesting.I: Have you done any work in this field?A:Yes, I have worked in this field for four years. First, I worked in an American company as a sales representative, then I transferred to a Hong Kong company as a sales manager.I: What kind of jobs have you had?A: I worked as a business coordinator in a foreign representative office, then I transferred to a joint venture as a sales manager. So I am familiar with the textile market in China.I: What qualifications do you have that make you feel you will be successful in your field?A: First, I think my technical background is helpful. I have enough knowledge to market the products of your company. Secondly, I have studied for four months in a Marketing Training Programme with satisfactory results. Finally, I have mastered the English Language. These qualifications will make me successful in my career.I: What have you learned from the jobs you have had?A: I have learned a lot about business know-how and basic office skills. In addition , I learned at my previous jobs how to cooperate with my colleagues.I: Can you get recommendations from your previous employers?A:Yes, I have brought them with me. Here they are.I; What's you major weak point?A: I haven't been involved in international business, so I don' t have any experience, but I have studied this course in the International Business Training Centre of the Shanghai Foreign Service Company.I: What are your greatest strengths?A I know a lot about how the Chinese economy works, and how business is done here. Secondly, I speak English fluently. I have no difficulty with language. And, I am a hardworker when I have something challenging to do.I: Please tell me about your working experience.A: I have five years experience in the chemical industry since I graduated from college. First of all I worked as an assistant engineer in the Chemical Industry Co. Three years later I transferred to ABC Chemict Industry Company. Now I am working in the Sale Department of that company.I: Does your present employer know you are looking another job?A: No, I haven't discussed my career plans with my resent employer, but I am sure he will understand.用英语介绍自己的优点1、good morning, my name is jack, it is really a great honor to have this opportunity for a interview, i would like to answer whatever you may raise, and i hope i can make a good performance today, eventually enroll in this prestigious university in september.2、now i will introduce myself briefly,i am 21 years old,born in heilongjiang province ,northeast of china,and i am curruently a senior student at beijing XX major is packaging i will receive my bachelor degree after my graduation in the past 4 years,i spend most of my time on study,i have passed CET4/6 with a ease. and i have acquired basic knowledge of packaging and publishing both in theory and in practice. besides, i have attend3、several packaging exhibition hold in Beijing, this is our advantage study here, i have taken a tour to some big factory and company. through these i have a deeply understanding of domestic packaging industry.4、compared to developed countries such as us, unfortunately, although we have made extraordinary progress since 1978,our packaging industry are still underdeveloped, mess, unstable, the situation of employees in this field are awkard. but i have full confidence in a bright future if only our economy can keep the growth pace still5、 guess you maybe interested in the reason itch to law, and what is my plan during graduate study life, i would like to tell you that pursue law is one of my lifelong goal,i like my major packaging and i wont give up,if i can pursue my master degree here i will combine law with my former education. i will work hard in thesefields ,patent ,trademark, copyright, on the base of my years study in department of p&p, my character? i cannot describe it well, but i know i am optimistic and confident. sometimes i prefer to stay alone, reading, listening to music, but i am not lonely, i like to chat with my classmates, almost talk everything ,my favorite pastime is valleyball,playing cards or surf online. through college life,i learn how to balance between study and entertainment. by the way, i was a actor of our amazing drama club. i had a few glorious memory on stage. that is my pride.推荐面试时的英语自我介绍General IntroductionI am a third year master major in automation at Shanghai Jiao Tong University, P. R. China. With tremendousinterest in Industrial Engineering, I am writing to apply for acceptance into your . graduate program.Education backgroundIn 1995, I entered the Nanjing University of Science & Technology (NUST) -- widely considered one of the China’s best engineering schools. During the following undergraduate study, my academic records kept distingui shed among the whole department. I was granted First Class Prize every semester, and my overall GPA100) ranked among 113 students. In 1999, I got the privilege to enter the graduate program waived of the admission test. I selected the Shanghai Jiao Tong University to continue my study for its best reputation on Combinatorial Optimization and Network Scheduling where my research interest lies.At the period of my graduate study, my overall GPA ranked top 5% in the department. In the second semester, I became teacher assistant that is given to talented and matured students only. This year, I won the Acer Scholarship as the one and only candidate in my department, which is the ultimate accolade for distinguished students endowed by my university. Presently, I am preparing my graduation thesis and trying for the honor of Excellent Graduation Thesis.Research experience and academic activityWhen a sophomore, I joined the Association of AI Enthusiast and began to narrow down my interest for my futur e research. In 1997, I participated in simulation tool development for the scheduling system in Prof. Wang’s lab. With the tool of OpenGL and Matlab, I designed a simulation program for transportation scheduling system. It is now widely used by different research groups in NUST. In 1998, I assumed and fulfilled a sewage analysis & dispose project for Nanjing sewage treatment plant. This was my first practice to convert a laboratory idea to a commercial product.In 1999, I joined the distinguished Professor Yu-Geng Xi's research group aiming at Network flow problem solving and Heuristic algorithm research. Soon I was engaged in the FuDan Gene Database Design. My duty was to pick up the useful information among different kinds of gene matching format. Through the comparison and analysis for many heuristic algorithms, I introduced an improved evolutionary algorithm -- Multi-population Genetic Algorithm. By dividing a whole population into several sub-populations, this improved algorithm can effectively prevent GA from local convergence and promote various evolutionary orientations. It proved more efficiently than SGA in experiments, too. In the second semester, I joined the workshop-scheduling research in Shanghai Heavy Duty Tyre plant. The scheduling was designed for the rubber-making process that covered not only discrete but also continuous circumstances. To make a balance point between optimization quality and time cost, I proposed a Dynamic Layered Scheduling method based on hybrid Petri Nets. The practical application showed that the average makespan was shortened by a large scale. I also publicized two papers in core journals with this idea. Recently, I am doing research in the Composite Predict of the Electrical Power system assisted with the technology of Data Mining for Bao Steel. I try to combine the Decision Tree with Receding Optimization to provide a new solution for the CompositePredictive Problem. This project is now under construction.Besides, In July 2000, I got the opportunity to give a lecture in English in Asia Control Conference (ASCC) which is one of the top-level conferences among the world in the area of control and automation. In my senior year, I met Prof. Xiao-Song Lin, a visiting professor of mathematics from University of California-Riverside, I learned graph theory from him for my network research. These experiences all rapidly expanded my knowledge of English and the understanding of western culture.I hope to study in depthIn retrospect, I find myself standing on a solid basis in both theory and experience, which has prepared me for the . program. My future research interests include: Network Scheduling Problem, Heuristic Algorithm research(especially in GA and Neural network), Supply chain network research, Hybrid system performance analysis withPetri nets and Data Mining.Please give my application materials a serious consideration. Thank you very much.面试时,我们会面对形形色色的问题,而最令人哑口无言的,往往是一些最简单和最常见的题目,比如"请你自我介绍一下"。
人工智能英汉
人工智能英汉Aβα-Pruning, βα-剪枝, (2) Acceleration Coefficient, 加速系数, (8) Activation Function, 激活函数, (4) Adaptive Linear Neuron, 自适应线性神经元,(4)Adenine, 腺嘌呤, (11)Agent, 智能体, (6)Agent Communication Language, 智能体通信语言, (11)Agent-Oriented Programming, 面向智能体的程序设计, (6)Agglomerative Hierarchical Clustering, 凝聚层次聚类, (5)Analogism, 类比推理, (5)And/Or Graph, 与或图, (2)Ant Colony Optimization (ACO), 蚁群优化算法, (8)Ant Colony System (ACS), 蚁群系统, (8) Ant-Cycle Model, 蚁周模型, (8)Ant-Density Model, 蚁密模型, (8)Ant-Quantity Model, 蚁量模型, (8)Ant Systems, 蚂蚁系统, (8)Applied Artificial Intelligence, 应用人工智能, (1)Approximate Nondeterministic Tree Search (ANTS), 近似非确定树搜索, (8) Artificial Ant, 人工蚂蚁, (8)Artificial Intelligence (AI), 人工智能, (1) Artificial Neural Network (ANN), 人工神经网络, (1), (3)Artificial Neural System, 人工神经系统,(3) Artificial Neuron, 人工神经元, (3) Associative Memory, 联想记忆, (4) Asynchronous Mode, 异步模式, (4) Attractor, 吸引子, (4)Automatic Theorem Proving, 自动定理证明,(1)Automatic Programming, 自动程序设计, (1) Average Reward, 平均收益, (6) Axon, 轴突, (4)Axon Hillock, 轴突丘, (4)BBackward Chain Reasoning, 逆向推理, (3) Bayesian Belief Network, 贝叶斯信念网, (5) Bayesian Decision, 贝叶斯决策, (3) Bayesian Learning, 贝叶斯学习, (5) Bayesian Network贝叶斯网, (5)Bayesian Rule, 贝叶斯规则, (3)Bayesian Statistics, 贝叶斯统计学, (3) Biconditional, 双条件, (3)Bi-Directional Reasoning, 双向推理, (3) Biological Neuron, 生物神经元, (4) Biological Neural System, 生物神经系统, (4) Blackboard System, 黑板系统, (8)Blind Search, 盲目搜索, (2)Boltzmann Machine, 波尔兹曼机, (3) Boltzmann-Gibbs Distribution, 波尔兹曼-吉布斯分布, (3)Bottom-Up, 自下而上, (4)Building Block Hypotheses, 构造块假说, (7) CCell Body, 细胞体, (3)Cell Membrane, 细胞膜, (3)Cell Nucleus, 细胞核, (3)Certainty Factor, 可信度, (3)Child Machine, 婴儿机器, (1)Chinese Room, 中文屋, (1) Chromosome, 染色体, (6)Class-conditional Probability, 类条件概率,(3), (5)Classifier System, 分类系统, (6)Clause, 子句, (3)Cluster, 簇, (5)Clustering Analysis, 聚类分析, (5) Cognitive Science, 认知科学, (1) Combination Function, 整合函数, (4) Combinatorial Optimization, 组合优化, (2) Competitive Learning, 竞争学习, (4) Complementary Base, 互补碱基, (11) Computer Games, 计算机博弈, (1) Computer Vision, 计算机视觉, (1)Conflict Resolution, 冲突消解, (3) Conjunction, 合取, (3)Conjunctive Normal Form (CNF), 合取范式,(3)Collapse, 坍缩, (11)Connectionism, 连接主义, (3) Connective, 连接词, (3)Content Addressable Memory, 联想记忆, (4) Control Policy, 控制策略, (6)Crossover, 交叉, (7)Cytosine, 胞嘧啶, (11)DData Mining, 数据挖掘, (1)Decision Tree, 决策树, (5) Decoherence, 消相干, (11)Deduction, 演绎, (3)Default Reasoning, 默认推理(缺省推理),(3)Defining Length, 定义长度, (7)Rule (Delta Rule), 德尔塔规则, 18(3) Deliberative Agent, 慎思型智能体, (6) Dempster-Shafer Theory, 证据理论, (3) Dendrites, 树突, (4)Deoxyribonucleic Acid (DNA), 脱氧核糖核酸, (6), (11)Disjunction, 析取, (3)Distributed Artificial Intelligence (DAI), 分布式人工智能, (1)Distributed Expert Systems, 分布式专家系统,(9)Divisive Hierarchical Clustering, 分裂层次聚类, (5)DNA Computer, DNA计算机, (11)DNA Computing, DNA计算, (11) Discounted Cumulative Reward, 累计折扣收益, (6)Domain Expert, 领域专家, (10) Dominance Operation, 显性操作, (7) Double Helix, 双螺旋结构, (11)Dynamical Network, 动态网络, (3)E8-Puzzle Problem, 八数码问题, (2) Eletro-Optical Hybrid Computer, 光电混合机, (11)Elitist strategy for ant systems (EAS), 精化蚂蚁系统, (8)Energy Function, 能量函数, (3) Entailment, 永真蕴含, (3) Entanglement, 纠缠, (11)Entropy, 熵, (5)Equivalence, 等价式, (3)Error Back-Propagation, 误差反向传播, (4) Evaluation Function, 评估函数, (6) Evidence Theory, 证据理论, (3) Evolution, 进化, (7)Evolution Strategies (ES), 进化策略, (7) Evolutionary Algorithms (EA), 进化算法, (7) Evolutionary Computation (EC), 进化计算,(7)Evolutionary Programming (EP), 进化规划,(7)Existential Quantification, 存在量词, (3) Expert System, 专家系统, (1)Expert System Shell, 专家系统外壳, (9) Explanation-Based Learning, 解释学习, (5) Explanation Facility, 解释机构, (9)FFactoring, 因子分解, (11)Feedback Network, 反馈型网络, (4) Feedforward Network, 前馈型网络, (1) Feasible Solution, 可行解, (2)Finite Horizon Reward, 横向有限收益, (6) First-order Logic, 一阶谓词逻辑, (3) Fitness, 适应度, (7)Forward Chain Reasoning, 正向推理, (3) Frame Problem, 框架问题, (1)Framework Theory, 框架理论, (3)Free-Space Optical Interconnect, 自由空间光互连, (11)Fuzziness, 模糊性, (3)Fuzzy Logic, 模糊逻辑, (3)Fuzzy Reasoning, 模糊推理, (3)Fuzzy Relation, 模糊关系, (3)Fuzzy Set, 模糊集, (3)GGame Theory, 博弈论, (8)Gene, 基因, (7)Generation, 代, (6)Genetic Algorithms, 遗传算法, (7)Genetic Programming, 遗传规划(遗传编程),(7)Global Search, 全局搜索, (2)Gradient Descent, 梯度下降, (4)Graph Search, 图搜索, (2)Group Rationality, 群体理性, (8) Guanine, 鸟嘌呤, (11)HHanoi Problem, 梵塔问题, (2)Hebbrian Learning, 赫伯学习, (4)Heuristic Information, 启发式信息, (2) Heuristic Search, 启发式搜索, (2)Hidden Layer, 隐含层, (4)Hierarchical Clustering, 层次聚类, (5) Holographic Memory, 全息存储, (11) Hopfield Network, 霍普菲尔德网络, (4) Hybrid Agent, 混合型智能体, (6)Hype-Cube Framework, 超立方体框架, (8)IImplication, 蕴含, (3)Implicit Parallelism, 隐并行性, (7) Individual, 个体, (6)Individual Rationality, 个体理性, (8) Induction, 归纳, (3)Inductive Learning, 归纳学习, (5) Inference Engine, 推理机, (9)Information Gain, 信息增益, (3)Input Layer, 输入层, (4)Interpolation, 插值, (4)Intelligence, 智能, (1)Intelligent Control, 智能控制, (1) Intelligent Decision Supporting System (IDSS), 智能决策支持系统,(1) Inversion Operation, 倒位操作, (7)JJoint Probability Distribution, 联合概率分布,(5) KK-means, K-均值, (5)K-medoids, K-中心点, (3)Knowledge, 知识, (3)Knowledge Acquisition, 知识获取, (9) Knowledge Base, 知识库, (9)Knowledge Discovery, 知识发现, (1) Knowledge Engineering, 知识工程, (1) Knowledge Engineer, 知识工程师, (9) Knowledge Engineering Language, 知识工程语言, (9)Knowledge Interchange Format (KIF), 知识交换格式, (8)Knowledge Query and ManipulationLanguage (KQML), 知识查询与操纵语言,(8)Knowledge Representation, 知识表示, (3)LLearning, 学习, (3)Learning by Analog, 类比学习, (5) Learning Factor, 学习因子, (8)Learning from Instruction, 指导式学习, (5) Learning Rate, 学习率, (6)Least Mean Squared (LSM), 最小均方误差,(4)Linear Function, 线性函数, (3)List Processing Language (LISP), 表处理语言, (10)Literal, 文字, (3)Local Search, 局部搜索, (2)Logic, 逻辑, (3)Lyapunov Theorem, 李亚普罗夫定理, (4) Lyapunov Function, 李亚普罗夫函数, (4)MMachine Learning, 机器学习, (1), (5) Markov Decision Process (MDP), 马尔科夫决策过程, (6)Markov Chain Model, 马尔科夫链模型, (7) Maximum A Posteriori (MAP), 极大后验概率估计, (5)Maxmin Search, 极大极小搜索, (2)MAX-MIN Ant Systems (MMAS), 最大最小蚂蚁系统, (8)Membership, 隶属度, (3)Membership Function, 隶属函数, (3) Metaheuristic Search, 元启发式搜索, (2) Metagame Theory, 元博弈理论, (8) Mexican Hat Function, 墨西哥草帽函数, (4) Migration Operation, 迁移操作, (7) Minimum Description Length (MDL), 最小描述长度, (5)Minimum Squared Error (MSE), 最小二乘法,(4)Mobile Agent, 移动智能体, (6)Model-based Methods, 基于模型的方法, (6) Model-free Methods, 模型无关方法, (6) Modern Heuristic Search, 现代启发式搜索,(2)Monotonic Reasoning, 单调推理, (3)Most General Unification (MGU), 最一般合一, (3)Multi-Agent Systems, 多智能体系统, (8) Multi-Layer Perceptron, 多层感知器, (4) Mutation, 突变, (6)Myelin Sheath, 髓鞘, (4)(μ+1)-ES, (μ+1) -进化规划, (7)(μ+λ)-ES, (μ+λ) -进化规划, (7) (μ,λ)-ES, (μ,λ) -进化规划, (7)NNaïve Bayesian Classifiers, 朴素贝叶斯分类器, (5)Natural Deduction, 自然演绎推理, (3) Natural Language Processing, 自然语言处理,(1)Negation, 否定, (3)Network Architecture, 网络结构, (6)Neural Cell, 神经细胞, (4)Neural Optimization, 神经优化, (4) Neuron, 神经元, (4)Neuron Computing, 神经计算, (4)Neuron Computation, 神经计算, (4)Neuron Computer, 神经计算机, (4) Niche Operation, 生态操作, (7) Nitrogenous base, 碱基, (11)Non-Linear Dynamical System, 非线性动力系统, (4)Non-Monotonic Reasoning, 非单调推理, (3) Nouvelle Artificial Intelligence, 行为智能,(6)OOccam’s Razor, 奥坎姆剃刀, (5)(1+1)-ES, (1+1) -进化规划, (7)Optical Computation, 光计算, (11)Optical Computing, 光计算, (11)Optical Computer, 光计算机, (11)Optical Fiber, 光纤, (11)Optical Waveguide, 光波导, (11)Optical Interconnect, 光互连, (11) Optimization, 优化, (2)Optimal Solution, 最优解, (2)Orthogonal Sum, 正交和, (3)Output Layer, 输出层, (4)Outer Product, 外积法, 23(4)PPanmictic Recombination, 混杂重组, (7) Particle, 粒子, (8)Particle Swarm, 粒子群, (8)Particle Swarm Optimization (PSO), 粒子群优化算法, (8)Partition Clustering, 划分聚类, (5) Partitioning Around Medoids, K-中心点, (3) Pattern Recognition, 模式识别, (1) Perceptron, 感知器, (4)Pheromone, 信息素, (8)Physical Symbol System Hypothesis, 物理符号系统假设, (1)Plausibility Function, 不可驳斥函数(似然函数), (3)Population, 物种群体, (6)Posterior Probability, 后验概率, (3)Priori Probability, 先验概率, (3), (5) Probability, 随机性, (3)Probabilistic Reasoning, 概率推理, (3) Probability Assignment Function, 概率分配函数, (3)Problem Solving, 问题求解, (2)Problem Reduction, 问题归约, (2)Problem Decomposition, 问题分解, (2) Problem Transformation, 问题变换, (2) Product Rule, 产生式规则, (3)Product System, 产生式系统, (3) Programming in Logic (PROLOG), 逻辑编程, (10)Proposition, 命题, (3)Propositional Logic, 命题逻辑, (3)Pure Optical Computer, 全光计算机, (11)QQ-Function, Q-函数, (6)Q-learning, Q-学习, (6)Quantifier, 量词, (3)Quantum Circuit, 量子电路, (11)Quantum Fourier Transform, 量子傅立叶变换, (11)Quantum Gate, 量子门, (11)Quantum Mechanics, 量子力学, (11) Quantum Parallelism, 量子并行性, (11) Qubit, 量子比特, (11)RRadial Basis Function (RBF), 径向基函数,(4)Rank based ant systems (ASrank), 基于排列的蚂蚁系统, (8)Reactive Agent, 反应型智能体, (6) Recombination, 重组, (6)Recurrent Network, 循环网络, (3) Reinforcement Learning, 强化学习, (3) Resolution, 归结, (3)Resolution Proof, 归结反演, (3) Resolution Strategy, 归结策略, (3) Reasoning, 推理, (3)Reward Function, 奖励函数, (6) Robotics, 机器人学, (1)Rote Learning, 机械式学习, (5)SSchema Theorem, 模板定理, (6) Search, 搜索, (2)Selection, 选择, (7)Self-organizing Maps, 自组织特征映射, (4) Semantic Network, 语义网络, (3)Sexual Differentiation, 性别区分, (7) Shor’s algorithm, 绍尔算法, (11)Sigmoid Function, Sigmoid 函数(S型函数),(4)Signal Function, 信号函数, (3)Situated Artificial Intelligence, 现场式人工智能, (1)Spatial Light Modulator (SLM), 空间光调制器, (11)Speech Act Theory, 言语行为理论, (8) Stable State, 稳定状态, (4)Stability Analysis, 稳定性分析, (4)State Space, 状态空间, (2)State Transfer Function, 状态转移函数,(6)Substitution, 置换, (3)Stochastic Learning, 随机型学习, (4) Strong Artificial Intelligence (AI), 强人工智能, (1)Subsumption Architecture, 包容结构, (6) Superposition, 叠加, (11)Supervised Learning, 监督学习, (4), (5) Swarm Intelligence, 群智能, (8)Symbolic Artificial Intelligence (AI), 符号式人工智能(符号主义), (3) Synapse, 突触, (4)Synaptic Terminals, 突触末梢, (4) Synchronous Mode, 同步模式, (4)TThreshold, 阈值, (4)Threshold Function, 阈值函数, (4) Thymine, 胸腺嘧啶, (11)Topological Structure, 拓扑结构, (4)Top-Down, 自上而下, (4)Transfer Function, 转移函数, (4)Travel Salesman Problem, 旅行商问题, (4) Turing Test, 图灵测试, (1)UUncertain Reasoning, 不确定性推理, (3)Uncertainty, 不确定性, (3)Unification, 合一, (3)Universal Quantification, 全称量词, (4) Unsupervised Learning, 非监督学习, (4), (5)WWeak Artificial Intelligence (Weak AI), 弱人工智能, (1)Weight, 权值, (4)Widrow-Hoff Rule, 维德诺-霍夫规则, (4)。
离散化代数重建的全变差算法改进
离散化代数重建的全变差算法改进张蕾;徐伯庆【摘要】This paper presents an improved discretization Algebraic Reconstruction algorithm which bases on Total Variation, mainly for discretization Algebraic Reconstruction (DART) algorithm is easily affected by factors such as noise and make the problem of image edge blurred, using Total Variation (TV) to minimize the constraints, an improved DART Reconstruction algorithm.Experiments show that the algorithm is compared with the traditional ART algorithm, can quickly reconstruct image, compared with the DART algorithm, improved the condition of image edge blur and has good noise resistance.%文中提出一种离散化代数重建的全变差改进算法,主要针对离散化的代数重建算法易受到噪声等因素的影响而使图像边缘较为模糊的问题,利用全变差最小化的约束条件,提出一种改进的DART 重建算法.实验表明,该算法与传统ART算法相比,能较快重建出图像,与DART算法相比,改善图像边缘模糊的情况,具有较好的抗噪性.【期刊名称】《电子科技》【年(卷),期】2016(029)012【总页数】5页(P121-125)【关键词】图像重建;DART;TV【作者】张蕾;徐伯庆【作者单位】上海理工大学光电信息与计算机工程学院,上海 200093;上海理工大学光电信息与计算机工程学院,上海 200093【正文语种】中文【中图分类】TP391.41计算机断层成像技术(CT)无论在医学放射诊断还是在工业领域均有着广泛应用[1]。
游戏设计理论
游戏设计理论第 1 章游戏定义游戏设计领域曾一度淹没在狂热的术语海洋中,最初出现的是视频游戏、计算机游戏以及一些相当简单的老虎机游戏,随后又出现了模拟游戏(Sim)、射击游戏(shooter)以及角色扮演游戏(Role Playing game,RPG)。
可是我们连基本的术语都没有弄明白。
我的字典使用6.5栏寸(column inch,报纸上登广告的尺寸,这里指用来解释某个术语的篇幅)来定义游戏(game),12 栏寸定义玩(Play)。
同时,这个字典仅使用3栏寸定义像吃(eat)这样普通的动词,仅使用1栏寸定义食物(food)。
关于游戏和玩的概念散播于整个知识界,它们几乎就像通用的全能动词get和go(各占14栏寸)一样过分普及。
面对这样失控的术语海洋,混淆很容易相伴而生。
因此,必须预先定义各个术语的意义。
当然,这并不是说本书给出的定义是最权威的,肯定会有其他人对某些术语做出不同的定义。
下面解释一下本书将如何使用这些术语。
图1.1 是各种不同术语的全景图。
图1.1 创造性表达的分类下面逐项介绍其中的每个术语。
“创造性表达”的概念相当广泛,足以包括人类可能感兴趣的所有作品。
可以理解为:“创作者的主要创作动机是什么?”假如创作者的主要目的是为了赚钱,那么就可以把作品的结果称为“娱乐”;如果创作者的主要目的是为了创造美好事物,那么就可以把它称为“艺术”。
进行这样的区分当然比较粗糙,不过使用在这里还是比较合适的。
关于艺术,还有大量更好的其他定义,但是这种定义的单纯性质更适合简单的头脑。
下面把话题从艺术转向娱乐。
娱乐的划分含义为:“它是否具有交互性?”假如不具有交互性,那么娱乐就和电影、电视、书籍、戏剧以及所有类似的东西属于同一类。
许多人在这些方面表现得很有才华。
另一个不够严谨的术语乐事(plaything)用于指任何种类的交互式娱乐。
当然,这不是一个印象深刻的术语,但只好勉为其难了。
对乐事的划分含义是:“从事这种乐事是否抱着相关的或明确的目标?” ,如果没有,就称之为玩具(toy)。
GAME THEORY MODELS(博弈模型) 尼科尔森中级微观ppt
• Each player has the ability to choose among a set of possible actions • The specific identity of the players is irrelevant
• This means that A will also choose to play music loudly • The A:L,B:L strategy choice obeys the criterion for a Nash equilibrium
• because L is a dominant strategy for B, it is the best choice no matter what A does • if A knows that B will follow his best strategy, then L is the best choice for A
The Prisoners’ Dilemma
• The most famous two-person game with an undesirable Nash equilibrium outcome
• games in which the strategies chosen by A and B are alternate levels of a single continuous variable • games where players use mixed strategies
19
Existence of Nash Equilibria
16
2019丘成桐中学科学奖分赛区比赛获奖名单
2019丘成桐中学科学奖分赛区比赛获奖名单2019丘成桐中学科学奖(数学奖)分赛区比赛获奖名单美国赛区金奖Choate Rosemary Hall参赛学生: Ziyan Lei 指导老师: Shuliang Bai论文题目: Title: Ricci-flat 5-regular graphs美国赛区银奖Westminster School参赛学生: Michelle Wu 指导老师: Steve Miller论文题目: Biases in First and Second Moments of the Fourier Coefficients in One- and Two-Parameter Families of Elliptic Curves美国赛区铜奖The Roxbury Latin School参赛学生: Christopher Zhu 指导老师: Pakawut Jiradilok论文题目: Enumerating Permutations and Rim Hooks Characterized by Double Descent Sets亚洲赛区(待宣)国内赛区一等奖1.北京市十一学校参赛学生:张子芃指导老师:张仲桓论文题目:Solving n-Player Games: a Combinatorial-Game-Theory Approach2.北京师范大学附属中学参赛学生:李新洋指导老师:赵昕、张亚论文题目:北京市西城区部分养老照料中心老人幸福感影响因素的研究3.北京大学附属中学参赛学生:许宸指导老师:单治超论文题目:随机区间图及其推广模型的性质研究4.北京师范大学附属实验中学参赛学生:张文翰、郑子阳指导老师:孙晓论文题目:A STUDY ON MÖBIUS FUNCTION AND EULER’S TOTIENT OF ORDER K5.华南师范大学附属中学国际部参赛学生:陈兆君指导老师:郝保国论文题目:围长为4的Ricci-平坦图6.福建省南平第一中学参赛学生:郑子航、刘彦熙、赵锃指导老师:包文涛论文题目:曲线定长包络线问题的研究7.重庆市巴蜀中学参赛学生:田鹏菲指导老师:肖佳劼论文题目:关于一个混合型均值不等式的探索8.华南师范大学附属中学参赛学生:李柏臻、林俊达指导老师:贾保国、申西芬论文题目:Sierpinski垫片的Hausdorff测度下界估算Bounds of the Solution for Sierpinski Gasket by Hausdorff measure9.杭州外国语学校参赛学生:董平川指导老师:姜海益、董浙论文题目:k-Primes and the Unique Factorization10.Shanghai Foreign Language School参赛学生:顾理指导老师:陈文俊论文题目:基于凸体堆积密度的Heilbronn问题分析11.杭州外国语学校参赛学生:章叔阳、潘越、陈君洋指导老师:倪瑞祥、朱灵论文题目:New Wilker-type Inequalities for Trigonometric Functions12.华东师范大学第二附属中学参赛学生:倪丹指导老师:戴中元论文题目:关于函数f(n)=δ(n)/n的几项探究Several problems about Function f(n)=Delta(n)/n国内赛区二等奖1.北京大学附属中学参赛学生:李雨彤指导老师:曹建莉论文题目:最优旅行目的地选择及旅行路径规划2.北京景山学校参赛学生:何雨濛、王子易、魏浚哲指导老师:许云尧论文题目:探究走班制分班排课方案3.北京市第八中学参赛学生:朱令闻指导老师:王文智论文题目:基于机器学习方法辨识肥胖人群痰湿体质的研究4.西安铁一中国际合作学校参赛学生:王晨雨、梁栋诚、鲁天舒指导老师:马睿、Matt McDonough 论文题目:储药柜优化设计问题的研究5.上海外国语大学附属外国语学校参赛学生:朱子毓指导老师:王尉平论文题目:Difference between Rational Periodic Points of Polynomial Dynamics 6.上海中学参赛学生:陈鹤升指导老师:来米加论文题目:平面全脐多边形的构建与分析7.上海平和双语学校参赛学生:李宇尧、邬京霖、何柯宇指导老师:祝鑫琰论文题目:Studies of Algebraic Integers Totally in a Compact Set8.江苏省泰州中学参赛学生:宋笑宇指导老师:宋健、顾建军论文题目:Proofs of two conjectures9.上海外国语大学附属外国语学校参赛学生:马鸣谦指导老师:吴东鹰论文题目:类欧拉无穷乘积与特殊函数的统一10.上海华东师范大学第二附属中学参赛学生:孙越阳指导老师:戴中元论文题目:对空间中立体几何相接数及其相关定理的研究11.华南师范大学附属中学参赛学生:苏俊全、钟振涛、朱哲霖指导老师:杨晓安、周正华论文题目:直角斜圆锥及直角斜圆锥曲线的数学性质12.广州市第六中学参赛学生:张紫嫣、陈正康指导老师:璩斌论文题目:基于位置与速度二次函数之和的瞬时泛函最小化问题13.深圳实验学校高中部参赛学生:顾羍圣、彭科霖指导老师:吕艳坤论文题目:圆锥曲线的一种新定义14.深圳外国语学校参赛学生:张若谷、林轩宇、李佳彭指导老师:袁智斌论文题目:论囚徒困境博弈的各种策略国内赛区三等奖1.清华大学附属中学参赛学生:杨尤拉指导老师:张广远、齐亚超论文题目:平面上有限点集共圆和构成正多边形顶点的充要条件2.北京市第八十中学参赛学生:林晓彬指导老师:Nathan论文题目:Cops and Robbers in the Secondary Neighborhood3.齐齐哈尔市第六中学校参赛学生:孙承鹏指导老师:尚炎葳论文题目:一个有关全体自然数的无穷乘积的有限表示4.中国人民大学附属中学ICC参赛学生:武彤桉指导老师:王思思论文题目:NBA MVP Prediction Model5.西安高新第一中学国际课程班参赛学生:韩毅指导老师:牛东杰论文题目:基于光流与惯导的无人机室内组合导航方法6.北京十一学校参赛学生:李欣航、刘桐宇、王筱舒指导老师:杜洋论文题目:优化纳米孔技术碱基分类正确性的数学建模方法7.浙江省新昌中学参赛学生:王尧勇、李颖、王可为指导老师:陈珈颖论文题目:对数独魔方及其性质的研究8.南京师范大学附属中学参赛学生:廖天乐、吴宜荟、柳一鸣指导老师:殷翔论文题目:Cracking Game 2048 Using Matrix Laboratory9.上海七宝德怀特高级中学参赛学生:江云峰、于洪简、沈文原指导老师:张拥军论文题目:精准射频消融术的数学理论10.杭州外国语参赛学生:王珂涵、林宇杭指导老师:兰冲论文题目:Functional transformation in the 2D and 3D spaces11.上海外国语大学附属外国语学校参赛学生:王芝任指导老师:胡逸论文题目:Modeling the tissue as self-assembly of notochord with sequential linear programming12.上海市世界外国语中学参赛学生:吴天洋指导老师:於嵩论文题目:(r,s,m)幂凸函数及类Fejer-Hadamard不等式13.杭州外国语学校参赛学生:童安泽、康佳辰、施懿珂指导老师:徐惠珍论文题目:莫比乌斯带模型建立以及其切割和拓扑学应用14.南京外国语学校国际部参赛学生:方语晨、张泽宇、张文韬指导老师:吴婷论文题目:民航重要发展因素的相关性分析15.南京外国语学校参赛学生:胡阳、陈金棋、刘哲显指导老师:黄志军、李世华论文题目:基于IR-UWB雷达传感器的地震废墟掩埋人员搜救方案的研究16.南京外国语学校参赛学生:王子琛指导老师:李铁香、林文伟论文题目:高速铁路轨道振动模型分析及快速计算17.南京外国语学校参赛学生:孙如璋、郑睿宸、陈艺冉指导老师:黄志军论文题目:The analysis of co-expression genes by MREE measure of NM-points18.四川师范大学附属中学参赛学生:王琦指导老师:李兴福论文题目:以排列组合思想求欧几里德空间高维锥体体积19.福建省南平第一中学参赛学生:应诺彤、卢诗喆指导老师:包文涛论文题目:基于直线、旋转运动的机构学转换原理分析及玩具仿真设计20.福建省南平第一中学参赛学生:谷佳缙、吴健坤指导老师:包文涛论文题目:新高考背景下非文理分科方案研究——以南平一中高一(4)班为例21.广东实验中学参赛学生:冯智衡、汤馨懿指导老师:杨辉论文题目:5G时代移动数据交易模型探讨22.四川省成都市航天中学参赛学生:杨志豪指导老师:梁燕论文题目:Weight of the Factors Restricting the Development of the Comprehensive Quality Evaluation in Sichuan in the Context of the New College Entrance Examination23.长沙麓山国际实验学校参赛学生:高楷麒指导老师:谌仁俊论文题目:中央环保督察对湖南生态文明建设暨湖南上市工业企业绩效的影响研究24.福建省厦门第一中学参赛学生:孙宁宁指导老师:陈慧妙论文题目:Demand Adaptive Multi-Objective Mathematical Optimization Method for Electric Taxi Fleet Dispatching with Carbon Emission Analysis25.深圳实验学校(高中部)参赛学生:柴博闻指导老师:蒋英论文题目:关于多项式函数积分与求和的研究26.深圳中学参赛学生:吴芊芊指导老师:刘斌直论文题目:快递员派送路线优化问题的研究27.福州第一中学参赛学生:许佳、陈仪、陈宇阳指导老师:郑林论文题目:地球承载人类生命的能力解决方案2019丘成桐中学科学奖(物理)分赛区比赛获奖名单美国赛区金奖Phillips Academy参赛学生: Victoria Zhang 指导老师: Bolun Chen论文题目: Patterns and Symmetries in Spiking Neural Networks亚洲赛区金奖The International School Bangalore, Bangalore, India参赛学生:Amogh PALASAMUDRAM指导老师:Bimal Ravindranathan 论文题目:The Automatic Walking Aid亚洲赛区银奖James Ruse Agricultural High School, Sydney, Australia参赛学生:Bob CHEN 指导老师:Sheila Pooviah论文题目:Investigating and Modelling Capacitive Power Transfer: An Innovative New Method for Faster Wireless Charging国内赛区一等奖1.大连枫叶国际学校参赛学生:王元秀指导老师:Brahim El fadil、Zhisong Lian论文题目:Investigating the Variation of the Sun’s Visual Shape, Atmospheric Refraction and the Einstein’s Special Relativity Considered2.北京市十一学校参赛学生:郭馨泽、赵聿雄指导老师:范博昭、黄俊论文题目:The Study of Unmanned Twin-body Asymmetric Flying-Airfoil Aircraft for Monitoring Air Quality3.学校:天津市耀华中学参赛学生:王卓杰、高宇成、项赟希指导老师:陈健、庞海论文题目:《流体碰撞加速问题研究》4.北京市第四中学参赛学生:陈泓铭指导老师:康帅、魏斌论文题目:运用光学方法判断植物油过氧化值的探究5.四川省温江中学参赛学生:卿慧指导老师:邱孝兵、邓慧文论文题目:对周期编织的雪糕棒链条崩离过程的动力学行为研究6.广州市第六中学参赛学生:薛博睿、陈一苇指导老师:璩斌论文题目:基于Tryman-Green干涉的透光流体温度场分布测量方法7.江苏省南京外国语学校参赛学生:彭澹明、樊亦扬、刘若辰指导老师:潘志民、王思慧论文题目:水波的时间反演现象的研究8.广东省华南师范大学附属中学国际部参赛学生:张亦涵指导老师:谢菠荪论文题目:钢琴弹奏方法与琴音声学特征的相关性研究9.四川省温江中学参赛学生:伍乐指导老师:邓慧文、邱孝兵论文题目:球状物体在漏斗形边界流场中的动态稳定状态10.广东省华南师范大学附属中学参赛学生:刘蕾指导老师:申西芬、吴玖丹论文题目:跑步者个体特征和跑步习惯对足部力学响应的影响研究Study on the influence of individual characteristics and running habits of runners on foot mechanical response11.浙江省杭州第十四中学参赛学生:王涵青指导老师:杨遥论文题目:超声波动态液位检测仪Ultrasonic dynamic level detector12.上海市复兴高级中学参赛学生:唐志杰指导老师:包鸣论文题目:对覆盖在强烈冷冻瓶口硬币跳动情况研究13.上海市华东师范大学第二附属中学参赛学生:孔攸文指导老师:姜淳论文题目:纳米调制结构薄膜的自发辐射/Spontaneous Emission from Modulated Nanofilm国内赛区二等奖1.北京第一〇一中学参赛学生:汤子瞻指导老师:詹光奕、欧智坚论文题目:Study of Beatbox Signal Analysis and Evaluation System2.中国人民大学附属中学参赛学生:侯行健指导老师:陈曦论文题目:Study of Slinky’s Motion of Tumbling Downstairs3.北京市私立汇佳学校参赛学生:张景淳、许鑫依指导老师:张斌珍论文题目:基于银杏叶表面微结构的柔性压力传感器研究4.江苏省南京外国语学校参赛学生:马泽宇、邢思成指导老师:钱征华、刘燕鸣论文题目:Electromagnetic Ultrasonic Transducer for Buried Depth Detection- the Working Mechanism, Optimization and Verification5.江苏省南京外国语学校参赛学生:陈睿劼、王舒逸、袁梓文指导老师:潘志明、杜菁论文题目:颗粒系统的稳定性研究6.江苏省南京市金陵中学参赛学生:宋昊东、宋昊南、钱睿哲指导老师:张丽敏、张启军论文题目:对绝缘体摩擦空气产生变化电场现象的研究7.江苏省南京外国语学校参赛学生:胡喆恺、倪雅晴、张千一指导老师:刘燕鸣论文题目:中世纪步兵的梦魇——英格兰长弓穿甲能力初探8.江苏省南京外国语学校参赛学生:蒋浩宇、欧泽飞指导老师:陈卓论文题目:高损耗材料薄膜干涉现象的研究2019丘成桐中学科学奖(化学)分赛区比赛获奖名单美国赛区金奖Princeton International School of Mathematics and Science参赛学生: Songtao Li 指导老师: Qiang Chen论文题目:Green Synthesis of Titanium Dioxide/Polymer Nanocomposites with Enhanced Catalytic Activity美国赛区银奖William Mason High School参赛学生: Justin Huang 指导老师: Betty Wang论文题目: Preparation of Reusable PVA-Nano TiO2 Foam For Wastewater Treatment亚洲赛区银奖The Chinese Foundation Secondary School, Hong Kong参赛学生:LEUNG Long Hei Ziv 指导老师:HO Chun Man论文题目:Wearable Textile-based Direct Urea Fuel Cell国内赛区一等奖1.大连育明高级中学参赛学生:陆鹏蓉指导老师:李先锋论文题目:咖啡渣基硬炭的制备及其作为钠离子电池负极材料的性能研究2.中国人民大学附属中学参赛学生:吴松泽指导老师:Sunil K. Sharma, Miriam Rafailovich论文题目:Sustainable Nanocellulose Membranes for Proton Exchange Membrane Fuel Cells 纳米纤维素薄膜在质子交换膜燃料电池中的应用3.中国人民大学附属中学参赛学生:梁天昊指导老师:杨硕、范克科论文题目:金属材料对抗酸雨腐蚀的智能“外衣”:对传统金属涂层的改进4.美国威利塔斯北京校区参赛学生:赵方浩指导老师:张仁哲、孙佳姝论文题目:Facile Synthesis of Carbon Quantum Dots with Green Fluorescent for Photocatalytic and Bioimaging Applications5.北京王府学校参赛学生:周逸霖、袁家宁、赵尔卓指导老师:高佳论文题目:On the Effect of the Modified Graphene/Graphite Composite Electrode in the Electricity Generation Efficiency of the Microbial Fuel Cell6.上海外国语大学附属外国语学校东校参赛学生:王馨仪指导老师:俞丽瑾论文题目:The redox-responsive release of cargos from ferrocene and cyclodextrin-capped mesoporous silica nanoparticles7.上海市浦东复旦附中分校参赛学生:李天羿指导老师:金石磊论文题目:Application of POM as a new 3D printing support material8.广州市天河外国语学校参赛学生:潘柏乐、李明康指导老师:Miriam Rafailovich, Yuan Xue论文题目:Synthesis of A Novel Flame-retardant Hydrogel for Skin Protection Using Xanthan Gum and Resorcinol Bis (diphenyl phosphate)-coated Starch9.南京外国语学校参赛学生:刘天辰、袁子晨、莫晗琦指导老师:许亮亮、陈晓君论文题目:Cu-based metal-organic frameworks HKUST-1 as an effective catalyst for highly sensitive determination of ascorbic acid10.安徽省合肥市第一中学美国高中参赛学生:汪文宇、钱由夫、Cassie Waner Huang 指导老师:汪志勇、黄伟新论文题目:One-pot Synthetic Method of Homoallylic Alcohol from Benzyl Alcohol under Electrocatalytic Condition11.华南师范大学附属中学国际部参赛学生:卢宇迪指导老师:谢程亮论文题目:Sequence-based QSAR study and biological evaluation of ACE inhibitory dipeptides as anti-hypertensive agents12.南京外国语学校参赛学生:张知为指导老师:郑佑轩、许亮亮论文题目:Fast synthesis of the iridium(III) complexes at room temperature for high-performance OLEDs国内赛区二等奖1.西安高新第一中学参赛学生:薛子钰指导老师:张乾论文题目:一种新型三唑类化合物的设计、化学合成与抑菌活性研究2.北京171中学参赛学生:张元震指导老师:曹葵论文题目:利用正交法研究三聚乙醛解聚回收乙醛3.中国人民大学附属中学参赛学生:张金荣指导老师:姜凤敏论文题目:A novel method for isolating nanocrystalline cellulose from eucalyptus hardwood4.华南师范大学附属中学参赛学生:余欣霖指导老师:朱芳、阮文红论文题目:基于酶@MOFs试纸-智能手机平台的葡萄糖即时分析系统5.南京外国语学校参赛学生:张子嘉指导老师:许亮亮论文题目:O2 -Evolving Upconversion Nanoparticles for Tri-Model Imaging-Guided Photothermal-Photodynamic Therapy6.华南师范大学附属中学/广州市天河外国语学校参赛学生:袁誉珊、袁英秦、徐思璐指导老师:黄勇潮、童叶翔论文题目:Bi3TaO7/Ti3C2 heterojunctions for efficiently enhanced photocatalytic performance for water-borne contaminant removal7.南京外国语学校参赛学生:窦雯、王倩奕、孙如璋指导老师:许亮亮、李承辉论文题目:A Scalable and Green Hydrophobic and Lipophilic Coating for Oil-Water Separation8.南京市第一中学参赛学生:胡珂指导老师:余定华论文题目:杂多酸离子液体催化消毒剂过氧乙酸的绿色合成工艺研究9.湖南省长沙市雅礼中学参赛学生:欧阳奕航指导老师:于雯、刘敏论文题目:Atomically Dispersed Cobalt on Graphitic Carbon Nitride for Enhanced Photocatalytic Carbon Dioxide Reduction Activity2019丘成桐中学科学奖(生物)分赛区比赛获奖名单美国赛区金奖Phillips Exeter Academy参赛学生: Neil Chowdhury 指导老师: Sameer Abraham论文题目: A method to recognize universal patterns in genome structure using Hi-C美国赛区银奖Phillips Academy参赛学生: Sarah Chen 指导老师: Tamara Ouspenskaia, Travis Law论文题目: Seeking Candidate Neoantigens from Retained Introns美国赛区铜奖The Harker School参赛学生: Gloria Zhuo Jia Zhang 指导老师: Danyang He, Ruihan Tang论文题目: Cytokine Receptor IL-33: ST-2 signaling developmentally regulates susceptibility to seizures, synaptic plasticity and microglial phagocytic activity亚洲赛区无国内赛区一等奖1.中国人民大学附属中学参赛学生:齐乐遥、阿丝娜指导老师:李峰论文题目:一种新模式植物——微萍的生物学特征研究和生活周期控制2.中国人民大学附属中学参赛学生:缪熹指导老师:姜凤敏、范文宏论文题目:不同粒径和官能团修饰的微塑料对水生生物食物链上的毒性影响研究3.新疆克拉玛依市第一中学参赛学生:何李祺、祁含钰、文香湘指导老师:宋珊珊论文题目:薰衣草提取物的抑菌性在鲜酿啤酒后处理中的应用4.北京市第一六一中学参赛学生:张曦文指导老师:窦非、张健翔论文题目:黄酮类化合物(ZGM1)对β淀粉样小肽的聚集影响及机制探究5.清华大学附属中学参赛学生:李昕一指导老师:吴晓磊、梁姝颋论文题目:强化聚乙烯塑料降解的微生物群落构建6.上海市实验学校参赛学生:朱薪宇指导老师:卢宝荣论文题目:杂草稻越冬秘密:钻进土壤的种子诱发了休眠性7.华南师范大学附属中学国际部参赛学生:王越洋指导老师:孙林冲论文题目:Pteryxin suppresses hepatocellular carcinoma by targeting HIF-1α and glucose metabolism8.华南师范大学附属中学参赛学生:成果、徐游新指导老师:栾云霞、罗宇立论文题目:昆虫呼吸蛋白的分子演化9.广州市第六中学参赛学生:于镇铨、林子健、陈贝琳指导老师:宋建陵、田新朋论文题目:深海环境中高产蛋白酶菌株的新发现10.成都外国语参赛学生:王珂、邓雅心、汤晟宇指导老师:彭锐论文题目:二甲双胍通过GSK3β/Wnt通路抑制涡虫再生11.南京外国语学校参赛学生:吴初阳、薛宇指导老师:郑建伟论文题目:地表O3增加对土壤碳转化微生物过程的影响国内赛区二等奖1.清华大学附属中学参赛学生:杜熹然、郝韵指导老师:王诗琦、姜成英论文题目:高效降解苯酚的重组大肠杆菌构建2.北京师范大学附属实验中学参赛学生:廖珈艺指导老师:庄林岚, 方韡论文题目:基于微生物功能强化的新型虹吸湿地污水净化研究3.北京大学附属中学参赛学生:王雨桐指导老师:张健旭论文题目:生长期睡眠不足对小鼠行为和神经递质产生较长的影响4.中国人民大学附属中学参赛学生:张矛盾指导老师:李伟、辇伟峰论文题目:利用定向进化筛选cas蛋白突变体以拓展其PAM识别范围5.山东省实验中学参赛学生:张馨洁、窦昕瑶指导老师:石磊论文题目:肠道菌群在高盐饮食引起的高血压发病中作用的动物研究6.西安高新第一中学参赛学生:王艺轩指导老师:马敏星论文题目:基于慢病毒感染的萤光标记细胞在肿瘤研究的构建及应用7.南京师范大学附属中学参赛学生:杨成指导老师:纪晓俊论文题目:CRISPR/Cas9基因组编辑技术在赤霉菌中的建立及其代谢工程应用8.华南师范大学附属中学参赛学生:李劲尤指导老师:张伟、杨晓安论文题目:海水与淡水中青鳉鱼(Oryzias melastigma)对无机砷解毒能力差异探究9.广东碧桂园国际学校参赛学生:谢松宏指导老师:陈东经论文题目:同源重组缺陷评分系统的优化10.上海交通大学附属中学参赛学生:王天宸指导老师:李一男论文题目:利用外分泌微小RNA实现肾癌早期无创性检测的方法初探11.安徽省合肥市第一中学参赛学生:史乐陶指导老师:吴毓婷、胡滟虹论文题目:Expression Changes and biological functions of long non-coding RNA H19 and its methylation in early alcoholic liver disease12.上海民办包玉刚实验学校参赛学生:高诚泽指导老师:沈旭论文题目:化合物Mas抑制RIPK逆转程序性坏死治疗阿尔茨海默症2019丘成桐中学科学奖(计算机)分赛区比赛获奖名单美国赛区金奖Danbury Math Academy参赛学生: Kenneth Choi, Tony Lee 指导老师: Xiaodi Wang论文题目: Differentially Private M-band Wavelet-Based Mechanisms in Machine Learning Environments美国赛区银奖Deerfield Academy参赛学生: Zhi Hua Yuk 指导老师: Shuliang Bai, Kyle Luh论文题目: Ricci Flow Approach to the School Bus Routing Problem亚洲赛区金奖Independent Schools Foundation Academy, Hong Kong参赛学生:WANG Yu Han Daisy指导老师:Yee Pan Angelo Leung, Jia Pan论文题目:Human-Friendly Autonomous Robot Navigation by Deep Reinforcement Learned Collision Avoidance亚洲赛区银奖River Valley High School, Singapore参赛学生:SI Chenglei 指导老师:Wu Kui, Aw Ai Ti论文题目:Sentiment Aware Neural Machine Translation国内赛区一等奖1.北京师范大学附属实验中学参赛学生:白行健指导老师:王鸿伟论文题目:Hateful User Detection with Adaptive Graph Convolutional Neural Networks2.中国人民大学附属中学参赛学生:王之枫、上官一凡、杨梅格指导老师:武迪论文题目:利用光传播模型和多次反射模型实现非视域成像3.北京师范大学附属实验中学参赛学生:王醇逸指导老师:黄河燕、崔福伟论文题目:基于多特征与多语言融合的语音情感识别算法研究4.Shanghai Starriver Bilingual School参赛学生:吕行健指导老师:张伟楠论文题目:Meta-Learning Algorithms for Multi-task Data Generation5.上海市上海中学参赛学生:赵海萌指导老师:沈孝山论文题目:CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders6.上海市上海中学参赛学生:傅易指导老师:王亚娟论文题目:模块化单片机开发系统7.南京外国语学校参赛学生:刘欣雨、马燕然、李博思指导老师:李曙、孙鑫论文题目:一种基于生成对抗网络的序列推荐方法8.南京外国语学校参赛学生:金川杨、王禹听、李滕昊指导老师:史钋镭论文题目:一种Sanger测序数据杂合突变识别算法9.杭州外国语学校参赛学生:张澄指导老师:许端清、张玮婧论文题目:面向儿童抽动症辅助治疗的多风格漫画头像智能生成国内赛区二等奖1.北京市第四中学参赛学生:蔡佳宏、贺昕妍、高宇轩指导老师:陈慧妙论文题目:A Method of EV Detour-to- Recharge Behavior Modeling and Charging Station Deployment2.北京大学附属中学参赛学生:刘知宜指导老师:肖然、尹建芹论文题目:基于计算机视觉的重复性动作计数研究3.长春吉大附中力旺实验中学参赛学生:李穆指导老师:屈宝峰论文题目:低成本校园WiFi音频传输方案探索4.西安高新第一中学国际课程中心参赛学生:菊可欣指导老师:许豆、闫金涛论文题目:一种基于多特征融合的黑色素瘤自动诊断研究5.华南师范大学附属中学参赛学生:叶耀文指导老师:黄秉刚、杜云飞论文题目:基于计算机视觉及双耳效应的盲人智能眼镜的研究6.深圳中学参赛学生:陈毅骏、陈良源指导老师:周宇论文题目:基于可穿戴设备和计算智能的篮球运动动作识别研究7.广州市第六中学参赛学生:王胤哲、陈润萌、宋益善指导老师:梁靖韵论文题目:基于ETW-BERT模型的中文网络虚假评论识别8.上海市平和学校参赛学生:周驰宇指导老师:程治淇、赵波论文题目:Magic Artist: An image generation method from bounding boxes and labels 9.苏州德威英国国际学校参赛学生:顧寧健、钟之羿指导老师:赵霞论文题目:基于监控视频的人体跌倒检测10.江苏省海门中学参赛学生:陈倪劼、吉浩成指导老师:吴钧烽论文题目:Recurrent Convolutional Neural Networks for Toxic Comments Detection 11.合肥一中参赛学生:丁雯琪指导老师:石松涛论文题目:Molecular scalar coupling constant prediction based on stacking network of Graph Convolutional Network and Transformer2019丘成桐中学科学奖(经济金融建模)分赛区比赛获奖名单美国赛区金奖Thomas Jefferson High School for Science and Technology参赛学生: Benjamin Kang 指导老师: James Unwin。
GAME THEORY
1.2.2 重复剔除的占优均衡
Definition:In a normal-form game, if for each player i , si" is i‟s dominant strategy, than we call the strategies profile (s1″, …, sn" ) the „dominantstrategy equilibrium‟.
1.2.1占优战略均衡
定义2:一个博弈G,若对博弈方i及所用s-i都有 ui (si*,s-i) > ui (si ‟,s-i),则称si*是si ‟的严格上 策, si ‟是si*的严格下策。 定义3:若在博弈G中对每个博弈方i都存在策 略si*是其它所有策略的严格上策,则称策略组 合s*=(s1*,s2*, … ,sn*)是G的上策均衡。
In the normal-form representation of a game ,each
player simultaneously chooses a strategy, and the combination of strategies chosen by the players determines a payoff for each player. Definition: The normal-form representation of an-n-player game specifies the players‟ strategy spaces S1 , … , Sn and their payoff functions u1 ,…, un. We denote this game
Game Theory 2
GAME THEORYThomas S.Ferguson Part II.Two-Person Zero-Sum Games1.The Strategic Form of a Game.1.1Strategic Form.1.2Example:Odd or Even.1.3Pure Strategies and Mixed Strategies.1.4The Minimax Theorem.1.5Exercises.2.Matrix Games.Domination.2.1Saddle Points.2.2Solution of All2by2Matrix Games.2.3Removing Dominated Strategies.2.4Solving2×n and m×2Games.2.5Latin Square Games.2.6Exercises.3.The Principle of Indifference.3.1The Equilibrium Theorem.3.2Nonsingular Game Matrices.3.3Diagonal Games.3.4Triangular Games.3.5Symmetric Games.3.6Invariance.3.7Exercises.4.Solving Finite Games.4.1Best Responses.4.2Upper and Lower Values of a Game.4.3Invariance Under Change of Location and Scale.4.4Reduction to a Linear Programming Problem.4.5Description of the Pivot Method for Solving Games.4.6A Numerical Example.4.7Exercises.5.The Extensive Form of a Game.5.1The Game Tree.5.2Basic Endgame in Poker.5.3The Kuhn Tree.5.4The Representation of a Strategic Form Game in Extensive Form.5.5Reduction of a Game in Extensive Form to Strategic Form.5.6Example.5.7Games of Perfect Information.5.8Behavioral Strategies.5.9Exercises.6.Recursive and Stochastic Games.6.1Matrix Games with Games as Components.6.2Multistage Games.6.3Recursive Games. -Optimal Strategies.6.4Stochastic Movement Among Games.6.5Stochastic Games.6.6Approximating the Solution.6.7Exercises.7.Continuous Poker Models.7.1La Relance.7.2The von Neumann Model.7.3Other Models.7.4Exercises.References.Part II.Two-Person Zero-Sum Games1.The Strategic Form of a Game.The individual most closely associated with the creation of the theory of games is John von Neumann,one of the greatest mathematicians of this century.Although others preceded him in formulating a theory of games-notably´Emile Borel-it was von Neumann who published in1928the paper that laid the foundation for the theory of two-person zero-sum games.Von Neumann’s work culminated in a fundamental book on game theory written in collaboration with Oskar Morgenstern entitled Theory of Games and Economic Behavior,1944.Other more current books on the theory of games may be found in the text book,Game Theory by Guillermo Owen,2nd edition,Academic Press,1982,and the expository book,Game Theory and Strategy by Philip D.Straffin,published by the Mathematical Association of America,1993.The theory of von Neumann and Morgenstern is most complete for the class of games called two-person zero-sum games,i.e.games with only two players in which one player wins what the other player loses.In Part II,we restrict attention to such games.We will refer to the players as Player I and Player II.1.1Strategic Form.The simplest mathematical description of a game is the strate-gic form,mentioned in the introduction.For a two-person zero-sum game,the payofffunction of Player II is the negative of the payoffof Player I,so we may restrict attention to the single payofffunction of Player I,which we call here L.Definition1.The strategic form,or normal form,of a two-person zero-sum game is given by a triplet(X,Y,A),where(1)X is a nonempty set,the set of strategies of Player I(2)Y is a nonempty set,the set of strategies of Player II(3)A is a real-valued function defined on X×Y.(Thus,A(x,y)is a real number for every x∈X and every y∈Y.)The interpretation is as follows.Simultaneously,Player I chooses x∈X and Player II chooses y∈Y,each unaware of the choice of the other.Then their choices are made known and I wins the amount A(x,y)from II.Depending on the monetary unit involved, A(x,y)will be cents,dollars,pesos,beads,etc.If A is negative,I pays the absolute value of this amount to II.Thus,A(x,y)represents the winnings of I and the losses of II.This is a very simple definition of a game;yet it is broad enough to encompass the finite combinatorial games and games such as tic-tac-toe and chess.This is done by being sufficiently broadminded about the definition of a strategy.A strategy for a game of chess,for example,is a complete description of how to play the game,of what move to make in every possible situation that could occur.It is rather time-consuming to write down even one strategy,good or bad,for the game of chess.However,several different programs for instructing a machine to play chess well have been written.Each program constitutes one strategy.The program Deep Blue,that beat then world chess champion Gary Kasparov in a match in1997,represents one strategy.The set of all such strategies for Player I is denoted by X.Naturally,in the game of chess it is physically impossible to describe all possible strategies since there are too many;in fact,there are more strategies than there are atoms in the known universe.On the other hand,the number of games of tic-tac-toe is rather small,so that it is possible to study all strategies andfind an optimal strategy for each ter,when we study the extensive form of a game,we will see that many other types of games may be modeled and described in strategic form.To illustrate the notions involved in games,let us consider the simplest non-trivial case when both X and Y consist of two elements.As an example,take the game called Odd-or-Even.1.2Example:Odd or Even.Players I and II simultaneously call out one of the numbers one or two.Player I’s name is Odd;he wins if the sum of the numbers if odd. Player II’s name is Even;she wins if the sum of the numbers is even.The amount paid to the winner by the loser is always the sum of the numbers in dollars.To put this game in strategic form we must specify X,Y and A.Here we may choose X={1,2},Y={1,2}, and A as given in the following table.II(even)yI(odd)x12 1−2+3 2+3−4A(x,y)=I’s winnings=II’s losses.It turns out that one of the players has a distinct advantage in this game.Can you tell which one it is?Let us analyze this game from Player I’s point of view.Suppose he calls‘one’3/5ths of the time and‘two’2/5ths of the time at random.In this case,1.If II calls‘one’,I loses2dollars3/5ths of the time and wins3dollars2/5ths of the time;on the average,he wins−2(3/5)+3(2/5)=0(he breaks even in the long run).2.If II call‘two’,I wins3dollars3/5ths of the time and loses4dollars2/5ths of the time; on the average he wins3(3/5)−4(2/5)=1/5.That is,if I mixes his choices in the given way,the game is even every time II calls ‘one’,but I wins20/c on the average every time II calls‘two’.By employing this simple strategy,I is assured of at least breaking even on the average no matter what II does.Can Player Ifix it so that he wins a positive amount no matter what II calls?Let p denote the proportion of times that Player I calls‘one’.Let us try to choose p so that Player I wins the same amount on the average whether II calls‘one’or‘two’.Then since I’s average winnings when II calls‘one’is−2p+3(1−p),and his average winnings when II calls‘two’is3p−4(1−p)Player I should choose p so that−2p+3(1−p)=3p−4(1−p)3−5p=7p−412p=7p=7/12.Hence,I should call‘one’with probability7/12,and‘two’with probability5/12.On theaverage,I wins−2(7/12)+3(5/12)=1/12,or813cents every time he plays the game,nomatter what II does.Such a strategy that produces the same average winnings no matter what the opponent does is called an equalizing strategy.Therefore,the game is clearly in I’s favor.Can he do better than813cents per gameon the average?The answer is:Not if II plays properly.In fact,II could use the same procedure:call‘one’with probability7/12call‘two’with probability5/12.If I calls‘one’,II’s average loss is−2(7/12)+3(5/12)=1/12.If I calls‘two’,II’s average loss is3(7/12)−4(5/12)=1/12.Hence,I has a procedure that guarantees him at least1/12on the average,and II has a procedure that keeps her average loss to at most1/12.1/12is called the value of the game,and the procedure each uses to insure this return is called an optimal strategy or a minimax strategy.If instead of playing the game,the players agree to call in an arbitrator to settle thisconflict,it seems reasonable that the arbitrator should require II to pay813cents to I.ForI could argue that he should receive at least813cents since his optimal strategy guaranteeshim that much on the average no matter what II does.On the other hand II could arguethat he should not have to pay more than813cents since she has a strategy that keeps heraverage loss to at most that amount no matter what I does.1.3Pure Strategies and Mixed Strategies.It is useful to make a distinction between a pure strategy and a mixed strategy.We refer to elements of X or Y as pure strategies.The more complex entity that chooses among the pure strategies at random in various proportions is called a mixed strategy.Thus,I’s optimal strategy in the game of Odd-or-Even is a mixed strategy;it mixes the pure strategies one and two with probabilities 7/12and5/12respectively.Of course every pure strategy,x∈X,can be considered as the mixed strategy that chooses the pure strategy x with probability1.In our analysis,we made a rather subtle assumption.We assumed that when a player uses a mixed strategy,he is only interested in his average return.He does not care about hismaximum possible winnings or losses—only the average.This is actually a rather drastic assumption.We are evidently assuming that a player is indifferent between receiving5 million dollars outright,and receiving10million dollars with probability1/2and nothing with probability1/2.I think nearly everyone would prefer the$5,000,000outright.This is because the utility of having10megabucks is not twice the utility of having5megabucks.The main justification for this assumption comes from utility theory and is treated in Appendix1.The basic premise of utility theory is that one should evaluate a payoffby its utility to the player rather than on its numerical monetary value.Generally a player’s utility of money will not be linear in the amount.The main theorem of utility theory states that under certain reasonable assumptions,a player’s preferences among outcomes are consistent with the existence of a utility function and the player judges an outcome only on the basis of the average utility of the outcome.However,utilizing utility theory to justify the above assumption raises a new difficulty. Namely,the two players may have different utility functions.The same outcome may be perceived in quite different ways.This means that the game is no longer zero-sum.We need an assumption that says the utility functions of two players are the same(up to change of location and scale).This is a rather strong assumption,but for moderate to small monetary amounts,we believe it is a reasonable one.A mixed strategy may be implemented with the aid of a suitable outside random mechanism,such as tossing a coin,rolling dice,drawing a number out of a hat and so on.The seconds indicator of a watch provides a simple personal method of randomization provided it is not used too frequently.For example,Player I of Odd-or-Even wants an outside random event with probability7/12to implement his optimal strategy.Since 7/12=35/60,he could take a quick glance at his watch;if the seconds indicator showed a number between0and35,he would call‘one’,while if it were between35and60,he would call‘two’.1.4The Minimax Theorem.A two-person zero-sum game(X,Y,A)is said to be afinite game if both strategy sets X and Y arefinite sets.The fundamental theorem of game theory due to von Neumann states that the situation encountered in the game of Odd-or-Even holds for allfinite two-person zero-sum games.Specifically,The Minimax Theorem.For everyfinite two-person zero-sum game,(1)there is a number V,called the value of the game,(2)there is a mixed strategy for Player I such that I’s average gain is at least V no matter what II does,and(3)there is a mixed strategy for Player II such that II’s average loss is at most V no matter what I does.This is one form of the minimax theorem to be stated more precisely and discussed in greater depth later.If V is zero we say the game is fair.If V is positive,we say the game favors Player I,while if V is negative,we say the game favors Player II.1.5Exercises.1.Consider the game of Odd-or-Even with the sole change that the loser pays the winner the product,rather than the sum,of the numbers chosen(who wins still depends on the sum).Find the table for the payofffunction A,and analyze the game tofind the value and optimal strategies of the players.Is the game fair?2.Player I holds a black Ace and a red8.Player II holds a red2and a black7.The players simultaneously choose a card to play.If the chosen cards are of the same color, Player I wins.Player II wins if the cards are of different colors.The amount won is a number of dollars equal to the number on the winner’s card(Ace counts as1.)Set up the payofffunction,find the value of the game and the optimal mixed strategies of the players.3.Sherlock Holmes boards the train from London to Dover in an effort to reach the continent and so escape from Professor Moriarty.Moriarty can take an express train and catch Holmes at Dover.However,there is an intermediate station at Canterbury at which Holmes may detrain to avoid such a disaster.But of course,Moriarty is aware of this too and may himself stop instead at Canterbury.Von Neumann and Morgenstern(loc.cit.) estimate the value to Moriarty of these four possibilities to be given in the following matrix (in some unspecified units).HolmesMoriartyCanterbury Dover Canterbury100−50 Dover0100What are the optimal strategies for Holmes and Moriarty,and what is the value?(His-torically,as related by Dr.Watson in“The Final Problem”in Arthur Conan Doyle’s The Memoires of Sherlock Holmes,Holmes detrained at Canterbury and Moriarty went on to Dover.)4.The entertaining book The Compleat Strategyst by John Williams contains many simple examples and informative discussion of strategic form games.Here is one of his problems.“I know a good game,”says Alex.“We pointfingers at each other;either onefinger or twofingers.If we match with onefinger,you buy me one Daiquiri,If we match with twofingers,you buy me two Daiquiris.If we don’t match I letyou offwith a payment of a dime.It’ll help pass the time.”Olaf appears quite unmoved.“That sounds like a very dull game—at least in its early stages.”His eyes glaze on the ceiling for a moment and his lipsflutterbriefly;he returns to the conversation with:“Now if you’d care to pay me42cents before each game,as a partial compensation for all those55-cent drinks I’llhave to buy you,then I’d be happy to pass the time with you.Olaf could see that the game was inherently unfair to him so he insisted on a side payment as compensation.Does this side payment make the game fair?What are the optimal strategies and the value of the game?2.Matrix Games —DominationA finite two-person zero-sum game in strategic form,(X,Y,A ),is sometimes called a matrix game because the payofffunction A can be represented by a matrix.If X ={x 1,...,x m }and Y ={y 1,...,y n },then by the game matrix or payoffmatrix we mean the matrix A =⎛⎝a 11···a 1n ......a m 1···a mn⎞⎠where a ij =A (x i ,y j ),In this form,Player I chooses a row,Player II chooses a column,and II pays I the entry in the chosen row and column.Note that the entries of the matrix are the winnings of the row chooser and losses of the column chooser.A mixed strategy for Player I may be represented by an m -tuple,p =(p 1,p 2,...,p m )of probabilities that add to 1.If I uses the mixed strategy p =(p 1,p 2,...,p m )and II chooses column j ,then the (average)payoffto I is m i =1p i a ij .Similarly,a mixed strategy for Player II is an n -tuple q =(q 1,q 2,...,q n ).If II uses q and I uses row i the payoffto I is n j =1a ij q j .More generally,if I uses the mixed strategy p and II uses the mixed strategy q ,the (average)payoffto I is p T Aq = m i =1 n j =1p i a ij q j .Note that the pure strategy for Player I of choosing row i may be represented as the mixed strategy e i ,the unit vector with a 1in the i th position and 0’s elsewhere.Similarly,the pure strategy for II of choosing the j th column may be represented by e j .In the following,we shall be attempting to ‘solve’games.This means finding the value,and at least one optimal strategy for each player.Occasionally,we shall be interested in finding all optimal strategies for a player.2.1Saddle points.Occasionally it is easy to solve the game.If some entry a ij of the matrix A has the property that(1)a ij is the minimum of the i th row,and(2)a ij is the maximum of the j th column,then we say a ij is a saddle point.If a ij is a saddle point,then Player I can then win at least a ij by choosing row i ,and Player II can keep her loss to at most a ij by choosing column j .Hence a ij is the value of the game.Example 1.A =⎛⎝41−3325016⎞⎠The central entry,2,is a saddle point,since it is a minimum of its row and maximum of its column.Thus it is optimal for I to choose the second row,and for II to choose the second column.The value of the game is 2,and (0,1,0)is an optimal mixed strategy for both players.For large m ×n matrices it is tedious to check each entry of the matrix to see if it has the saddle point property.It is easier to compute the minimum of each row and the maximum of each column to see if there is a match.Here is an example of the method.row min A =⎛⎜⎝3210012010213122⎞⎟⎠0001col max 3222row min B =⎛⎜⎝3110012010213122⎞⎟⎠0001col max 3122In matrix A ,no row minimum is equal to any column maximum,so there is no saddle point.However,if the 2in position a 12were changed to a 1,then we have matrix B .Here,the minimum of the fourth row is equal to the maximum of the second column;so b 42is a saddle point.2.2Solution of All 2by 2Matrix Games.Consider the general 2×2game matrix A = a b d c.To solve this game (i.e.to find the value and at least one optimal strategy for each player)we proceed as follows.1.Test for a saddle point.2.If there is no saddle point,solve by finding equalizing strategies.We now prove the method of finding equalizing strategies of Section 1.2works when-ever there is no saddle point by deriving the value and the optimal strategies.Assume there is no saddle point.If a ≥b ,then b <c ,as otherwise b is a saddle point.Since b <c ,we must have c >d ,as otherwise c is a saddle point.Continuing thus,we see that d <a and a >b .In other words,if a ≥b ,then a >b <c >d <a .By symmetry,if a ≤b ,then a <b >c <d >a .This shows thatIf there is no saddle point,then either a >b ,b <c ,c >d and d <a ,or a <b ,b >c ,c <d and d >a .In equations (1),(2)and (3)below,we develop formulas for the optimal strategies and value of the general 2×2game.If I chooses the first row with probability p (es the mixed strategy (p,1−p )),we equate his average return when II uses columns 1and 2.ap +d (1−p )=bp +c (1−p ).Solving for p ,we findp =c −d (a −b )+(c −d ).(1)Since there is no saddle point,(a−b)and(c−d)are either both positive or both negative; hence,0<p<1.Player I’s average return using this strategy isv=ap+d(1−p)=ac−bda−b+c−d.If II chooses thefirst column with probability q(es the strategy(q,1−q)),we equate his average losses when I uses rows1and2.aq+b(1−q)=dq+c(1−q)Hence,q=c−ba−b+c−d.(2)Again,since there is no saddle point,0<q<1.Player II’s average loss using this strategyisaq+b(1−q)=ac−bda−b+c−d=v,(3)the same value achievable by I.This shows that the game has a value,and that the players have optimal strategies.(something the minimax theorem says holds for allfinite games). Example2.A=−233−4p=−4−3−2−3−4−3=7/12q=samev=8−9−2−3−4−3=1/12Example3.A=0−1012p=2−10+10+2−1=1/11q=2+100+10+2−1=12/11.But q must be between zero and one.What happened?The trouble is we“forgot to test this matrix for a saddle point,so of course it has one”.(J.D.Williams The Compleat Strategyst Revised Edition,1966,McGraw-Hill,page56.)The lower left corner is a saddle point.So p=0and q=1are optimal strategies,and the value is v=1.2.3Removing Dominated Strategies.Sometimes,large matrix games may be reduced in size(hopefully to the2×2case)by deleting rows and columns that are obviously bad for the player who uses them.Definition.We say the i th row of a matrix A=(a ij)dominates the k th row if a ij≥a kj for all j.We say the i th row of A strictly dominates the k th row if a ij>a kj for all j.Similarly,the j th column of A dominates(strictly dominates)the k th column if a ij≤a ik(resp.a ij<a ik)for all i.Anything Player I can achieve using a dominated row can be achieved at least as well using the row that dominates it.Hence dominated rows may be deleted from the matrix.A similar argument shows that dominated columns may be removed.To be more precise,removal of a dominated row or column does not change the value of a game .However,there may exist an optimal strategy that uses a dominated row or column (see Exercise 9).If so,removal of that row or column will also remove the use of that optimal strategy (although there will still be at least one optimal strategy left).However,in the case of removal of a strictly dominated row or column,the set of optimal strategies does not change.We may iterate this procedure and successively remove several rows and columns.As an example,consider the matrix,A .The last column is dominated by the middle column.Deleting the last column we obtain:A =⎛⎝204123412⎞⎠Now the top row is dominated by the bottomrow.(Note this is not the case in the original matrix).Deleting the top row we obtain:⎛⎝201241⎞⎠This 2×2matrix does not have a saddle point,so p =3/4,q =1/4and v =7/4.I’s optimal strategy in the original game is(0,3/4,1/4);II’s is (1/4,3/4,0).1241 A row (column)may also be removed if it is dominated by a probability combination of other rows (columns).If for some 0<p <1,pa i 1j +(1−p )a i 2j ≥a kj for all j ,then the k th row is dominated by the mixed strategy that chooses row i 1with probability p and row i 2with probability 1−p .Player I can do at least as well using this mixed strategy instead of choosing row k .(In addition,any mixed strategy choosing row k with probability p k may be replaced by the one in which k ’s probability is split between i 1and i 2.That is,i 1’s probability is increased by pp k and i 2’s probability is increased by (1−p )p k .)A similar argument may be used for columns.Consider the matrix A =⎛⎝046574963⎞⎠.The middle column is dominated by the outside columns taken with probability 1/2each.With the central column deleted,the middle row is dominated by the combination of the top row with probability 1/3and the bottom row with probability 2/3.The reducedmatrix, 0693,is easily solved.The value is V =54/12=9/2.Of course,mixtures of more than two rows (columns)may be used to dominate and remove other rows (columns).For example,the mixture of columns one two and threewith probabilities 1/3each in matrix B =⎛⎝135340223735⎞⎠dominates the last column,and so the last column may be removed.Not all games may be reduced by dominance.In fact,even if the matrix has a saddle point,there may not be any dominated rows or columns.The 3×3game with a saddle point found in Example 1demonstrates this.2.4Solving 2×n and m ×2games.Games with matrices of size 2×n or m ×2may be solved with the aid of a graphical interpretation.Take the following example.p 1−p 23154160Suppose Player I chooses the first row with probability p and the second row with proba-bility 1−p .If II chooses Column 1,I’s average payoffis 2p +4(1−p ).Similarly,choices of Columns 2,3and 4result in average payoffs of 3p +(1−p ),p +6(1−p ),and 5p respectively.We graph these four linear functions of p for 0≤p ≤1.For a fixed value of p ,Player I can be sure that his average winnings is at least the minimum of these four functions evaluated at p .This is known as the lower envelope of these functions.Since I wants to maximize his guaranteed average winnings,he wants to find p that achieves the maximum of this lower envelope.According to the drawing,this should occur at the intersection of the lines for Columns 2and 3.This essentially,involves solving the game in which II is restrictedto Columns 2and 3.The value of the game 3116is v =17/7,I’s optimal strategy is (5/7,2/7),and II’s optimal strategy is (5/7,2/7).Subject to the accuracy of the drawing,we conclude therefore that in the original game I’s optimal strategy is (5/7,2/7),II’s is (0,5/7,2/7,0)and the value is 17/7.Fig 2.10123456col.3col.1col.2col.4015/7pThe accuracy of the drawing may be checked:Given any guess at a solution to a game,there is a sure-fire test to see if the guess is correct ,as follows.If I uses the strategy (5/7,2/7),his average payoffif II uses Columns 1,2,3and 4,is 18/7,17/7,17/7,and 25/7respectively.Thus his average payoffis at least17/7no matter what II does.Similarly, if II uses(0,5/7,2/7,0),her average loss is(at most)17/7.Thus,17/7is the value,and these strategies are optimal.We note that the line for Column1plays no role in the lower envelope(that is,the lower envelope would be unchanged if the line for Column1were removed from the graph). This is a test for domination.Column1is,in fact,dominated by Columns2and3taken with probability1/2each.The line for Column4does appear in the lower envelope,and hence Column4cannot be dominated.As an example of a m×2game,consider the matrix associated with Figure2.2.If q is the probability that II chooses Column1,then II’s average loss for I’s three possible choices of rows is given in the accompanying graph.Here,Player II looks at the largest of her average losses for a given q.This is the upper envelope of the function.II wants tofind q that minimizes this upper envelope.From the graph,we see that any value of q between1/4and1/3inclusive achieves this minimum.The value of the game is4,and I has an optimal pure strategy:row2.Fig2.2⎛⎝q1−q154462⎞⎠123456row1row2row3011/41/2qThese techniques work just as well for2×∞and∞×2games.2.5Latin Square Games.A Latin square is an n×n array of n different letters such that each letter occurs once and only once in each row and each column.The5×5 array at the right is an example.If in a Latin square each letter is assigned a numerical value,the resulting matrix is the matrix of a Latin square game.Such games have simple solutions.The value is the average of the numbers in a row,and the strategy that chooses each pure strategy with equal probability1/n is optimal for both players.The reason is not very deep.The conditions for optimality are satisfied.⎛⎜⎜⎜⎝a b c d eb e acd c a de b d c e b ae d b a c ⎞⎟⎟⎟⎠a =1,b =2,c =d =3,e =6⎛⎜⎜⎜⎝1233626133313623362163213⎞⎟⎟⎟⎠In the example above,the value is V =(1+2+3+3+6)/5=3,and the mixed strategy p =q =(1/5,1/5,1/5,1/5,1/5)is optimal for both players.The game of matching pennies is a Latin square game.Its value is zero and (1/2,1/2)is optimal for both players.2.6Exercises.1.Solve the game with matrix−1−3−22 ,that is find the value and an optimal (mixed)strategy for both players.2.Solve the game with matrix 02t 1for an arbitrary real number t .(Don’t forget to check for a saddle point!)Draw the graph of v (t ),the value of the game,as a function of t ,for −∞<t <∞.3.Show that if a game with m ×n matrix has two saddle points,then they have equal values.4.Reduce by dominance to 2×2games and solve.(a)⎛⎜⎝5410432−10−1431−212⎞⎟⎠(b)⎛⎝1007126476335⎞⎠.5.(a)Solve the game with matrix 3240−21−45 .(b)Reduce by dominance to a 3×2matrix game and solve:⎛⎝08584612−43⎞⎠.6.Players I and II choose integers i and j respectively from the set {1,2,...,n }for some n ≥2.Player I wins 1if |i −j |=1.Otherwise there is no payoff.If n =7,for example,the game matrix is⎛⎜⎜⎜⎜⎜⎜⎜⎝0100000101000001010000010100000101000001010000010⎞⎟⎟⎟⎟⎟⎟⎟⎠。
进化游戏理论包-EvolutionaryGames 进化游戏理论的核心概念说明书
Package‘EvolutionaryGames’October12,2022Type PackageTitle Important Concepts of Evolutionary Game TheoryVersion0.1.2Maintainer Jochen Staudacher<*******************************>Description Evolutionary game theory applies game theory to evolving populations in biology,see e.g.one of the books by Weibull(1994,ISBN:978-0262731218)or by Sandholm(2010,ISBN:978-0262195874)for more details.A comprehensiveset of tools to illustrate the core concepts of evolutionary game theory,such as evolutionary stability or various evolutionary dynamics,for teachingand academic research is provided.License GPL-2Encoding UTF-8Imports deSolve(>=1.14),geometry(>=0.3-6),ggplot2(>=2.2.1),grDevices(>=3.2.2),interp(>=1.0-29),MASS(>=7.3-43),reshape2(>=1.4.2)Suggests knitr,rmarkdown,rglRoxygenNote7.1.2VignetteBuilder knitrNeedsCompilation noAuthor Daniel Gebele[aut,cph],Jochen Staudacher[aut,cre,cph]Repository CRANDate/Publication2022-08-2900:10:02UTCR topics documented:BNN (2)BR (3)ESS (4)ESset (5)ILogit (6)12BNN Logit (7)MSReplicator (8)phaseDiagram2S (9)phaseDiagram3S (10)phaseDiagram4S (11)Replicator (12)Smith (13)triangle (14)Index15 BNN Brown-von Neumann-Nash dynamicDescriptionBrown-von Neumann-Nash replicator dynamic as a type of evolutionary dynamics.UsageBNN(time,state,parameters)Argumentstime Regular sequence that represents the time sequence under which simulation takes place.state Numeric vector that represents the initial state.parameters Numeric vector that represents parameters needed by the dynamic.ValueNumeric list.Each component represents the rate of change depending on the dynamic.Author(s)Daniel Gebele<******************>ReferencesBrown,G.W.and von Neumann,J.(1950)"Solutions of games by differential equations",In: Kuhn,Harold William and Tucker,Albert William(Eds.)"Contributions to the Theory of Games I",Princeton University Press,pp.73–79.Examplesdynamic<-BNNA<-matrix(c(0,-2,1,1,0,-2,-2,1,0),3,byrow=TRUE)state<-matrix(c(0.4,0.3,0.3),1,3,byrow=TRUE)phaseDiagram3S(A,dynamic,NULL,state,FALSE,FALSE)BR3 BR BR dynamicDescriptionBest response dynamic as a type of evolutionary dynamics.UsageBR(time,state,parameters)Argumentstime Regular sequence that represents the time sequence under which simulation takes place.state Numeric vector that represents the initial state.parameters Numeric vector that represents parameters needed by the dynamic.ValueNumeric list.Each component represents the rate of change depending on the dynamic.Author(s)Daniel Gebele<******************>ReferencesGilboa,I.and Matsui,A.(1991)"Social Stability and Equilibrium",Econometrica59,pp.859–867.Examplesdynamic<-BRA<-matrix(c(0,-2,1,1,0,-2,-2,1,0),3,byrow=TRUE)state<-matrix(c(0.4,0.3,0.3),1,3,byrow=TRUE)phaseDiagram3S(A,dynamic,NULL,state,FALSE,FALSE)4ESS ESS ESS for two-player games with a maximum of three strategiesDescriptionComputes Evolutionary Stable Strategies of a game with two players and a maximum of three strategies.UsageESS(A,strategies=c(),floats=TRUE)ArgumentsA Numeric matrix of size2x2or3x3representing the number of strategies of asymmetric matrix game.strategies String vector of length n that names all strategies whereas n represents the num-ber of strategies.floats Logical value that handles number representation.If set to TRUE,floating-point arithmetic will be used,otherwise fractions.Default is TRUE.ValueNumeric matrix.Each row represents an ESS.Author(s)Daniel Gebele<******************>ReferencesSmith,J.M.and Price,G.R.(1973)"The logic of animal conflict",Nature246,pp.15–18.ExamplesESS(matrix(c(-1,4,0,2),2,byrow=TRUE),c("Hawk","Dove"),FALSE)ESS(matrix(c(1,2,0,0,1,2,2,0,1),3,byrow=TRUE))ESset5 ESset Evolutionarily stable set for two-player games with three strategiesDescriptionComputes evolutionarily stable sets of a game with two players and three strategies.UsageESset(A,strategies=c("1","2","3"),floats=TRUE)ArgumentsA Numeric matrix of size3x3representing the number of strategies of a symmetricmatrix game.strategies String vector of length3that names all strategies.floats Logical value that handles number representation.If set to TRUE,floating-point arithmetic will be used,otherwise fractions.Default is TRUE.ValueNumeric matrix.Each row represents the start and end point of a line(ESset).In addition,a plot of the ESset in the game will be created.Author(s)Daniel Gebele<******************>ReferencesThomas,B.(1985)"On evolutionarily stable sets",Journal of Mathematical Biology22,pp.105–115.Examples#Please note that the computation of evolutionarily stable sets#is rather time-consuming.#Depending on your machine you might need to wait more#than10seconds in order to run the following example.##Not run:A<-matrix(c(-2,5,10/9,0,5/2,10/9,-10/9,35/9,10/9),3,byrow=TRUE)strategies<-c("Hawk","Dove","Mixed ESS")ESset(A,strategies)##End(Not run)6ILogit ILogit ILogit dynamicDescriptionImitative Logit dynamic as a type of evolutionary dynamics.UsageILogit(time,state,parameters)Argumentstime Regular sequence that represents the time sequence under which simulation takes place.state Numeric vector that represents the initial state.parameters Numeric vector that represents parameters needed by the dynamic.ValueNumeric list.Each component represents the rate of change depending on the dynamic.Author(s)Jochen Staudacher<*******************************>ReferencesWeibull,J.W.(1997)"Evolutionary Game Theory",MIT Press.Examplesdynamic<-ILogitA<-matrix(c(-1,0,0,0,-1,0,0,0,-1),3,byrow=TRUE)state<-matrix(c(0.1,0.2,0.7,0.2,0.7,0.1,0.9,0.05,0.05),3,3,byrow=TRUE) eta<-0.7phaseDiagram3S(A,dynamic,eta,state,TRUE,FALSE)Logit7 Logit Logit dynamicDescriptionLogit dynamic as a type of evolutionary dynamics.UsageLogit(time,state,parameters)Argumentstime Regular sequence that represents the time sequence under which simulation takes place.state Numeric vector that represents the initial state.parameters Numeric vector that represents parameters needed by the dynamic.ValueNumeric list.Each component represents the rate of change depending on the dynamic.Author(s)Daniel Gebele<******************>ReferencesFudenberg,D.and Levine,D.K.(1998)"The Theory of Learning in Games",MIT Press.Examplesdynamic<-LogitA<-matrix(c(0,-2,1,1,0,-2,-2,1,0),3,byrow=TRUE)state<-matrix(c(0.4,0.3,0.3),1,3,byrow=TRUE)eta<-0.1phaseDiagram3S(A,dynamic,eta,state,FALSE,FALSE)8MSReplicator MSReplicator Maynard Smith replicator dynamicDescriptionMaynard Smith replicator dynamic as a type of evolutionary dynamics.UsageMSReplicator(time,state,parameters)Argumentstime Regular sequence that represents the time sequence under which simulation takes place.state Numeric vector that represents the initial state.parameters Numeric vector that represents parameters needed by the dynamic.ValueNumeric list.Each component represents the rate of change depending on the dynamic.Author(s)Daniel Gebele<******************>ReferencesSmith,J.M.(1982)"Evolution and the Theory of Games",Cambridge University Press.Examplesdynamic<-MSReplicatorA<-matrix(c(0,-2,1,1,0,-2,-2,1,0),3,byrow=TRUE)state<-matrix(c(0.4,0.3,0.3),1,3,byrow=TRUE)phaseDiagram3S(A,dynamic,NULL,state,FALSE,FALSE)phaseDiagram2S Phase Diagram for two-player games with two strategiesDescriptionPlots phase diagram of a game with two players and two strategies.UsagephaseDiagram2S(A,dynamic,params=NULL,vectorField=TRUE,strategies=c("1","2"))ArgumentsA Numeric matrix of size2x2representing the number of strategies of a symmetricmatrix game.dynamic Function representing an evolutionary dynamic.params Numeric vector representing additional parameters for the evolutionary dynamic.vectorField Logical value that handles vectorfield presentation.If set to TRUE,vectorfield will be shown,otherwise not.Default is TRUE.strategies String vector of length2that names all strategies.ValueNone.Author(s)Daniel Gebele<******************>ExamplesA<-matrix(c(-1,4,0,2),2,2,byrow=TRUE)phaseDiagram2S(A,Replicator,strategies=c("Hawk","Dove"))phaseDiagram3S Phase Diagram for two-player games with three strategiesDescriptionPlots phase diagram of a game with two players and three strategies.UsagephaseDiagram3S(A,dynamic,params=NULL,trajectories=NULL,contour=FALSE,vectorField=FALSE,strategies=c("1","2","3"))ArgumentsA Numeric matrix of size3x3representing the number of strategies of a symmetricmatrix game.dynamic Function representing an evolutionary dynamic.params Numeric vector with additional parameters for the evolutionary dynamic.trajectories Numeric matrix of size mx3.Each row represents the initial values for the tra-jectory to be examined.contour Logical value that handles contour diagram presentation.If set to TRUE,contour diagram will be shown,otherwise not.Default is FALSE.vectorField Logical value that handles vectorfield presentation.If set to TRUE,vectorfield will be shown,otherwise not.Default is FALSE.strategies String vector of length3that names all strategies.ValueNone.Author(s)Daniel Gebele<******************>phaseDiagram4S11ExamplesA<-matrix(c(0,-2,1,1,0,-2,-2,1,0),3,byrow=TRUE)state<-matrix(c(0.4,0.3,0.3),1,3,byrow=TRUE)phaseDiagram3S(A,Replicator,NULL,state,FALSE,FALSE)phaseDiagram3S(A,Replicator,NULL,state,TRUE,TRUE)#Plot two trajectories rather than only one:A<-matrix(c(0,-2,1,1,0,-2,-2,1,0),3,byrow=TRUE)state<-matrix(c(0.4,0.3,0.3,0.6,0.2,0.2),2,3,byrow=TRUE)phaseDiagram3S(A,Replicator,NULL,state,FALSE,FALSE)phaseDiagram4S Phase Diagram for two-player games with four strategiesDescriptionPlots phase diagram of a game with two players and four strategies.UsagephaseDiagram4S(A,dynamic,params=NULL,trajectory=NULL,strategies=c("1","2","3","4"),noRGL=TRUE)ArgumentsA Numeric matrix of size4x4representing the number of strategies of a symmetricmatrix game.dynamic Function representing an evolutionary dynamic.params Numeric vector with additional parameters for the evolutionary dynamic.trajectory Numeric vector of size4representing the initial value for the trajectory to be examined.strategies String vector of length4that names all strategies.noRGL Logical value that handles diagram rotation.If set to FALSE,diagram will be rotatable,otherwise not.Default is TRUE.ValueNone.12ReplicatorAuthor(s)Daniel Gebele<******************>ExamplesA<-matrix(c(5,-9,6,8,20,1,2,-18,-14,0,2,20,13,0,4,-13),4,4,byrow=TRUE)state<-c(0.3,0.2,0.1,0.4)phaseDiagram4S(A,Replicator,NULL,state)Replicator Replicator dynamicDescriptionReplicator dynamic as a type of evolutionary dynamics.UsageReplicator(time,state,parameters)Argumentstime Regular sequence that represents the time sequence under which simulation takes place.state Numeric vector that represents the initial state.parameters Numeric vector that represents parameters needed by the dynamic.ValueNumeric list.Each component represents the rate of change depending on the dynamic.Author(s)Daniel Gebele<******************>ReferencesTaylor,P.D.and Jonker,L.B.(1978)"Evolutionary stable strategies and game dynamics",Mathe-matical Biosciences40(1-2),pp.145–156.Examplesdynamic<-ReplicatorA<-matrix(c(0,-2,1,1,0,-2,-2,1,0),3,byrow=TRUE)state<-matrix(c(0.4,0.3,0.3),1,3,byrow=TRUE)phaseDiagram3S(A,dynamic,NULL,state,FALSE,FALSE)Smith13 Smith Smith dynamicDescriptionSmith dynamic as a type of evolutionary dynamics.UsageSmith(time,state,parameters)Argumentstime Regular sequence that represents the time sequence under which simulation takes place.state Numeric vector that represents the initial state.parameters Numeric vector that represents parameters needed by the dynamic.ValueNumeric list.Each component represents the rate of change depending on the dynamic.Author(s)Daniel Gebele<******************>ReferencesSmith,M.J.(1984)"The Stability of a Dynamic Model of Traffic Assignment–An Application ofa Method of Lyapunov",Transportation Science18,pp.245–252.Examplesdynamic<-SmithA<-matrix(c(0,-2,1,1,0,-2,-2,1,0),3,byrow=TRUE)state<-matrix(c(0.4,0.3,0.3),1,3,byrow=TRUE)phaseDiagram3S(A,dynamic,NULL,state,FALSE,FALSE)14triangle triangle Triangle for2-simplex operationsDescriptionGenerates a triangle representing the2-simplex.Usagetriangle(labels=c("1","2","3"))Argumentslabels String vector of length3that names the edges of the triangle.ValueList of size2with members coords and canvas.coords holds edge coordinates of the2-simplex, canvas a ggplot2plot object of the2-simplex.Author(s)Daniel Gebele<******************>Examplestriangle()IndexBNN,2BR,3ESS,4ESset,5ILogit,6Logit,7MSReplicator,8phaseDiagram2S,9phaseDiagram3S,10phaseDiagram4S,11Replicator,12Smith,13triangle,1415。
ICPC EC-final出题风格
ICPC EC-final出题风格ICPC EC-final出题风格的比赛形式一般是五个小时八个题目,综合考察选手的数学能力、算法能力、coding能力和debug能力,还有团队配合能力。
数学方面主要强调组合数学、图论和数论这三个方面的能力;而算法的覆盖范围很广,涉及了大部分经典的算法,和少量较前沿的算法。
由于每道题目都需要通过所有的测试数据才能得分,并且需要精确解,这限制了Approximation algorithm在一些NP-hard的题目中的运用,从而使得搜索和剪枝策略对于NP-hard的题目非常重要。
Final的题目和Regional题目的比较ACM ICPC官方的正式比赛可分为World Final和Regional Contest 两种。
Final的题目更加正统和严谨,强调算法的综合运用,一个题目往往需要几种算法的结合。
从这几年的final的题目看,final加大了题目的代码量,对代码能力的要求有所增强。
而Regional的题目则更加灵活,同时每个赛区也有自己的出题风格。
欧洲赛区的题目以高质量出名,对算法和数学的强调甚至超过了World Final;美国的赛区较多模拟题,强调代码量。
而亚洲则介于两者之间,同时由于每年都有一些新的赛区,所以并没有很固定的模式。
下面浅谈一下近几年ACM ICPC的题目的覆盖面。
一些常规的算法和题型没什么好讲的,下面主要侧重一些新颖的知识点或题型,或是一些较前沿的内容。
数学的新题型除了一些基本的组合数学和组合数论的问题,近年来概率和Combinatorial Game Theory的题目逐渐增多。
很多有趣的题目都是以Markov Process为背景,需要用到一些相关的知识。
去年国内杭州赛区的一个很有趣的题目是,给出一个字符集(比如{A,B,C})和一个字符串T(比如ACBBCAC),现在从一个空串S开始,每次等概率的添加A,B,C中的一个字符,直到T是S的一个子串。
带时间延迟的极小化总完工时间的单机排序问题
带时间延迟的极小化总完工时间的单机排序问题胡觉亮;王焕男;蒋义伟【摘要】研究工件带有两道工序的单台机排序问题.在该问题中,工件的第一道工序先于第二道工序加工,并且第二道工序的开工时间与第一道工序的完工时间至少间隔一定的延迟时间,目标是极小化所有工件的总完工时间.文章考虑所有工件相同且两道工序的加工时间均为单位时间的情形.通过引入k-连续加工的概念和分析最优解的性质,根据延迟时间的大小,分别设计了两个算法并证明了算法所得的排序为最优排序.【期刊名称】《浙江理工大学学报》【年(卷),期】2014(031)001【总页数】5页(P83-87)【关键词】单台机;时间延迟;总完工时间;算法设计与分析;最优排序【作者】胡觉亮;王焕男;蒋义伟【作者单位】浙江理工大学理学院,杭州310018;浙江理工大学理学院,杭州310018;浙江理工大学理学院,杭州310018【正文语种】中文【中图分类】O233本文主要研究带延迟时间的单机排序问题。
每个工件Jj有两道工序aj和bj,第一道工序先于第二道工序加工,第一道工序的完工时间与第二道工序的开工时间之间至少存在lj个单位时间延迟,也就是说第二道工序至少等待lj个单位时间才能开工。
此类问题在一些产品制造工艺流程、布匹印染和服装订单的生产中有重要的应用背景。
对于带有延迟的排序问题,主要分为两类,一类是工件Jj前后两道工序的延迟时间恰好为lj,本文称之为精确延迟排序;另一类是两道工序间的延迟时间至少为lj,称之为至少延迟排序。
关于精确延迟的排序问题,Orman等[1]证明了单台机的一些特殊情况是多项式可解的,并证明即便所有工序的加工时间相同,该问题还是强NP-难的。
Leung等[2]利用贪婪算法研究延迟非增的情形,给出了问题F2|,aj=a,bj=b,a≥b|∑Cj的最优排序,并对问题F2|,aj=a,bj=b,a<b|∑Cj给出了一个2-近似算法。
Ageev等[3]分别研究了单台机和两台流水作业机器问题的一些近似算法。
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Combinatorial Game Theory WorkshopE.Berlekamp(Berkeley),M.Mueller(University of Alberta),R.J.Nowakowski(Dalhousie University),D.Wolfe(Gustavus Adolphus College).June18to June23,2005A main aim of the workshop was to bring together the two camps,mathematicians working in combinatorial game theory and computer scientists interested in algorithmics and Artificial intelli-gence.The Workshop attracted a mix of people from both communities(17from mathematics,16from computer science and2undergraduates)as well as a mixture of new and established researchers. The oldest was Richard Guy,turning90in2006and the youngest was in3rd year University.There were attendees from Europe,Asia as well as North America.The Workshop succeeded in its primary goal and more.New collaborations were struck.There was quick dissemination and evaluation of major new results and new results were developed during the Workshop.Part of the success was due to the staffand facilities at BIRS.The facilities at BIRS were appreciated by all the participants.The main room allowed lectures to mix computer presentations with overheads and chalkboard calculations.(No prizes for guessing which community used which technology.)The coffee lounges and break away rooms allowed dis-cussions to continue on,in comfort,until late in the night.Our thanks go to all the staffwho made the stay such a wonderful experience and to the BIRS organization for hosting the workshop.The elder statesmen of the community,Berlekamp,Conway,Fraenkel and Guy,all took active roles in the proceedings.Thefirst three gave survey talks on various topics and all were involved in discussions throughout the days and the evenings.The younger(established)generation were represented by the likes of Demaine,Grossman,M¨u ller and Siegel.As befits a workshop on combinatorial games,games were invented,played and analyzed.Philoso-phers Football(Phutball)was much in evidence.There was a Konane tournament played over three evenings.Much effort went into attempting an analysis of Sticky Towers of Hanoi,a game invented at the Workshop by Conway,spearheaded by Conway and the youngest attendee,Alex Fink.There were many representatives of the Go community quite a few of whom had never met each other.Collaboration is very important in this community.For example,David Wolfe presented a progress report on work of G.A.Mesdal on a class of partizan splitting games,answering questions first raised over30years ago.Mesdal is a joint effort of eleven co-authors from North America and New Zealand.Eight of the eleven attended the workshop and the number of co-authors had risen to twelve by the time the Workshop ended.In the end,between the talks and the discussions,there was simply too much to absorb in such a short time.The talks,surveys and several consequent papers are slated to appear as(tentatively titled)Games of No Chance3in the MSRI book series.1All the presentations were at a high standard and all had lively discussions during and after the time allotted.Some highlights were:Conway’s talk on lexicodes;Berlekamp’s overview Today’s View of Combinatorial Game Theory;and Fraenkel’s What hides beyond the curtain separating Nim from non-Nim games;Demaine’s talk on Dyson Telescopes and Moving Coin Puzzles also showed the complexity in some very new and some very old puzzles.However the highlights were the reports by:1.Plambeck on a breakthrough in the analysis of impartial mis`e r games;2.Siegel on extending the analysis of loopy games;3.Friedman and Landsberg on applying renormalization techniques from physics to combinatorialgames—this paper was both controversial and thought provoking and lead to the most discus-sions,including ones on the nature of truth and of proof;4.Nakamura on the use of‘cooling by2’to determine the winner in‘races to capture’in Go.Oneof the goals of the Workshop was to bring together researchers from mathematics and computer science.This was one of the talks that helped bridge the gap and engendered much discussion.The30minutes allotted to the talk was too short,and most participants stayed an extra hour (into the dinner-time)so as to hear the details.All of these were very new,very important results,produced only months before the Workshop.1:Mis`e re Games:On page146of On Numbers and Games,in Chapter12,“How to Lose When You Must,”John Conway writes:Note that in a sense,[mis´e re]restive games are ambivalent Nim-heaps,which choose their size(g0or g)according to their company.There are many other games which exhibitbehaviour of this type,and it would be very interesting to have some general theory forthem.Questions about the analysis of mis´e re impartial octal games were raised in[3,6]and no good general analytical techniques have been developed apart fromfinding the genus sequence[3].(See [1,22],see also[8,20]).In his presentation,Plambeck provided such a general theory,cast in the language of commutative semigroups.The mis`e re analysis of a combinatorial game often proves to be far more difficult than its normal play version.In fact it is an open question(Plambeck)if there is a mis´e re impartial game whose analysis is simpler than the normal play version and there is no know way of analyzing mis´e re partizan games([15],Problem9).To take a typical example,the normal play of Dawsons Chess was solved as early as1956by Guy and Smith[16],but even today,a complete mis`e re analysis has not been found.Guy tells the story[15]:“[Dawsons chess]is played on a3×n board with white pawns on thefirst rank and black pawns on the third.It was posed as a losing game(last-player-losing,now called mis`e re)so that capturing was obligatory.Fortunately,(because we still don‘t know how to play Mis`e re Dawsons Chess)I assumed,as a number of writers of that time and since have done,that the mis`e re analysis required only a trivial adjustment of the normal(last-player-winning)analysis.This arises because Bouton, in his original analysis of Nim[5],had observed that only such a trivial adjustment was necessary to cover both normal and mis`e re play...”But even for impartial games,in which the same options are available to both players,regardless of whose turn it is to move,Grundy&Smith[14]showed that the general situation in mis`e re play soon gets very complicated,and Conway[6],(p.140)confirmed that the situation can only be simplified to the microscopically small extent noticed by Grundy&Smith.In Chapter13of[3],the genus theory of impartial mis`e re disjunctive sums is extended signifi-cantly from its original presentation in chapter7(How to Lose When You Must)of Conways On Numbers and Games[6].But excluding the tame games that play like Nim in mis`e re play,theres a remarkable paucity of example games that the genus theory completely resolves.For example,the section Mis`e re Kayles([3],pg411)promises,“Although several tame games arise in Kayles(see Chapter4),wild games abounding and well need all our[genus-theoretic]resources to tackle it...”However,it turns out Kayles wasn‘t tackled at all.It was left to the amateur William L.Sibert to settle mis`e re Kayles using completely different methods.Onefinds a description of his solution at end of the updated Chapter13in[4],and also in[22].When normal play is in effect,every game with nimber G+(G)=k can be thought of as the nim heap k.No information about best play of the game is lost by assuming that G is in fact precisely the nim heap of size k.Moreover,in normal play,the nimber of a sum is just the nim-sum of the nimbers of the summands.In this sense every normal play impartial game position is simply a disguised version of Nim(see[3],Chapter4,for a full discussion).Genera.When mis`e re play is in effect,nimbers can still be defined but many inequivalent games are assigned the same nimber,and the outcome of a sum is not determined by nimber of the summands.These unfortunate facts lead directly to the apparent great complexity of many mis`e re analyses.Nevertheless progress can be made.The key definition,taken directly from[6],now at the bottom of page141:In the analysis of many games,we need even more information than is provided by either of these values[G+and G−],and so we shall define a more complicated symbol that we call the G o-value or genus.This is the symbolg.g0g1g2...where g=G+(G),g0=G−(G),g1=G−(G+2),g2=G−(G+2+2),...,where,in general,g n is the G−-value of the sum of G with n other games all equal to[the nim-heap of size]2.Atfirst sight,the genus symbol looks to be an potentially infinitely long symbol in its exponent.In practice,it can be shown that the g i s always fall into an eventual period two pattern.By convention, the symbol is written down with afinite exponent with the understanding that itsfinal two values repeat indefinitely.Evidently the exponent of a genus symbol of a game G is closely related to the outcome of sums of G with all multiples of mis`e re nim heaps of size two.The genus computations are intended to illustrate the complexities of a mis`e re analysis when the only tools available to be applied are those described in Chapter13of Winning Ways.Plambeck’s breakthrough was to introduce a quotient semigroup structure on the set of all posi-tions of an impartial game withfixed rules.The basic construction is the same for both normal and mis`e re play.In normal play,it leads to the familiar Sprague-Grundy theory.In mis`e re play,when applied to the set of all sums of positions played according to a particular game’s rules,it leads to a quotient of a free commutative semigroup by the game’s indistinguishability congruence.Playing a role similar to the one that nim sequences do for normal play,mappings from single-heap positions into a game’s mis`e re quotient semigroup succinctly and necessarily encode all relevant information about its best mis`e re play.Plambeck showed examples of wild mis`e re games that involve an infinity of ever-more complicated canonical forms amongst their position sums that may nevertheless possess a relatively simple,evenfinite mis`e re quotient.SupposeΓis a taking and breaking game whose rules have beenfixed in advance.Let h i be a distinct,purely formal symbol for each i≥1.We will call the set H={h1,h2,h3,...}the heap alphabet.A particular symbol h i will sometimes be called a heap of size i.The notation H n stands for the subset H n={h1,...,h n}⊆H for each n≥1.Let F H be the free commutative semigroup on the heap alphabet H.The semigroups F Hand F Hn include an identityΛ,which is just the empty word.There’s a natural correspondencebetween the elements of F H and the set of all position sums of a taking and breaking gameΓ.In this correspondence,afinite sum of heaps of various sizes is written multiplicatively using corresponding elements of the heap alphabet H.This multiplicative notation for sums makes it convenient to take the convention that the empty positionΛ=1.It corresponds to the endgame—a position with no options.Fix the rules and associated play convention(normal or mis`e re)of a particular taking and breaking gameΓ.Let u,v∈F H be game positions inΓ.We’ll say that u is indistinguishable from v over F H,and write the relation uρv,if for every element w∈F H,uw and vw are either both P-positions,or are both N-positions.Lemma1The relationρis a congruence on F H.Suppose the rules and play convention of a taking and breaking gameΓarefixed,and letρbe the indistinguishability congruence on F H,the free commutative semigroup of all positions inΓ. The indistinguishability quotient Q=Q(Γ)is the commutative semigroupQ=F H/ρ.Notice that the indistinguishability quotient can be taken with respect to either play convention (normal or mis`e re).The details of the indistinguishability congruence then determine the structure of the indistinguishability quotient.Since the word“indistinguishability”is quite a mouthful,Q is called the quotient semigroup ofΓ.WhenΓis a normal play game,its quotient semigroup Q=Q(Γ) is more than just a semigroup.A re-interpretation of the Sprague-Grundy theory says that these are always groups,each isomorphic to a direct product of a(possibly infinite)set of Z2’s(cyclic groups of order two).If u is a position in F H with normal play nim-heap equivalent∗k,the members of a particular congruence class uρ∈F H/ρwill be precisely all positions that have normal-play nim-heap equivalent∗k.The identity of Q is the congruence class of all positions with nim-heap equivalent ∗0.The“group multiplication”corresponds to nim addition.For mis`e re play,the quotient structure is a semigroup.Surprisingly,it’s often afinite object,even for a game that has an infinite number of different canonical forms occurring amongst its sums.The elements of a particular congruence class all have the same outcome.Each class can be thought of as carrying a big stamp labelled “P”(previous player wins in best play for all positions in this class)or“N”(next player wins).In normal play,there’s only one equivalence class labelled“P”—these are the positions with nim heap equivalent∗0.In mis`e re play,for all but the trivial games with one position∗0,or two positions {∗0,∗1},there is always more than one“P”class—one corresponding to the position∗1,and at least one more,corresponding to the position∗2+∗2.At the time of the presentation,Plambeck had20games each of whose octal description was short but whose analysis had defied his attempts.Plambeck offered varying amounts of money for their solutions.During the Workshop,Aaron Siegel solved four of them and,in conjunction with Plambeck,has solved all of the games and produced a computer program that helps with representations of the quotient semi-groups.2:Loopy Games.Aaron Siegel reported on two parts of the work contained in his PhD thesis, this particular presentation concerned loopy games.The traditional theory of combinatorial games assumes that no position may be repeated.This restriction guarantees that arbitrary sums of games will terminate;the result is a clean,recursive,and computationally efficient theory.However,there are many interesting games that allow repetition,including Fox and Geese,Hare and Hounds,Back-sliding Toads and Frogs,Phutball and Checkers.Go is a peculiar example:the ko rule forbids most repeated positions,but local repetition is extremely important when the board must be decomposed to effect a tractable analysis.Every game that permits repeated positions faces the possibility of nonterminating play.This is typically resolved by declaring infinite plays drawn(as in Checkers and Chess),but alternative resolutions are not uncommon.For example,Hare and Hounds declares infinite plays wins for the Hare,and some dialects of Go rules forbid them altogether.The disjunctive theory,in its most general form,assumes that in sums withinfinite play,the game is drawn unless the same player wins on every component in which play is nonterminating.This is vacuously true for games where infinite play is drawn to begin with,and it applies equally well to games such as Hare and Hounds. Go,with its unique ko rule,does notfit so cleanly into the theory.The general disjunctive theory wasfirst considered by Robert Li[17],who in the mid-1970s focused on games where it is a disadvantage to move,including a variant of Hackenbush.Shortly thereafter,Conway,together with his students Clive Bach and Simon Norton,generalized and cod-ified the theory and coined the term loopy game.Their results,including the fundamental concepts of stoppers and sides,appearedfirst in a1978paper[6]and were reprinted in Winning Ways.At roughly the same time,Shaki[21]and Fraenkel and Tassa[13]studied approximations and reduc-tions of partizan loopy games under a slightly different set of assumptions.Despite thisflurry of initial activity,there were few advances in the two decades following thefirst publication of WinningWays.Moews generalization of sidling was a rare exception:Published in his1993thesis[18,19],it constituted thefirst real advance in the disjunctive theory since the late1970s.Various authors have studied loopy games in other contexts.Generalizations of the Sprague-Grundy theory to impartial loopy games were introduced by Smith[23]a full decade before Li in-vented the partizan theory.They were studied in the1970s by Fraenkel and Perl[11]and Conway[3], and much more recently by Fraenkel and Rahat[12].James Flanigan,in his1979thesis and two subsequent papers[9,10],analyzed conjunctive and selective sums of partizan loopy games.Meanwhile,the greatest advance of the1990s came from an entirely different quarter,the study of kos in Go.The interplay between local cycles and the global state of the position gives rise to a rich and fascinating temperature theory,which appears to differ from Conways disjunctive theory in striking ways.The theory wasfirst realized by Berlekamp,following his analysis of loop free Go positions with his student David Wolfe(see[2].Many others have since investigated the theory of kos,including Fraser,M¨u ller,Nakamura,Spight and Takizawa.(See[25,24,27,28],for some examples.)Siegel showed how to calculate canonical forms of loopy games and gave some of their characteris-tics.One of his remarkable achievements is the software package CGSuite(for the“computationally efficient theory”offinite disjunctive sums)and then and its extension to be able to calculate the canonical form of loopy games.Siegel,Ottaway and Nowakowski showed how rich the canonical forms of small games can be when they considered1-dimensional Phutball played on boards of length7,8,9,10,and11.3:Cooling and Go.The applications of combinatorial game theory to the game of Go have, so far,been focused on endgames and eyespace values.A capturing race is a particular kind of life and death problem in which both of the two adjacent opposing groups arefighting to capture the opponent’s group each other.Skills in winning races are very important factor to the strength of Go as well as openings and endgames techniques.In order to win the complicated capturing races, techniques of counting liberties,taking away the opponent’s liberties and extending own liberties in addition to wide and deep reading are necessary.Nakamura,”“On Counting Liberties in Capturing Races of Go”showed that the‘counting’required can be regarded as combinatorial game with a score.Within this framework,he showed how to analyze capturing races that have no shared liberty or have just simple shared liberties using combinatorial game values of external liberties and an evaluation formula tofind out the outcome of the capturing races.Essentially,the evaluation formula is by cooling.All applications of cooling so far have been chilling(cooling by1)but in this case,one must cool by2!4:Renormalization techniques.Friedman&Landsberg presented a new approach to combinatorial games that unveiled connec-tions between such games and nonlinear phenomena commonly seen in nature:scaling behaviors, complex dynamics and chaos,growth and aggregation ing the game of Chomp(as well as variants of the game of Nim)as prototypes,they showed that the game possesses an underlying geometric structure that grows(reminiscent of crystal growth),and showed how this growth can be analyzed using a renormalization procedure.This approach not only obtains answers to some open questions about the game of Chomp,but opens a new line of attack for understanding(at least some)combinatorial games more generally through their underlying connection to nonlinear science.Analysis of these two-player games has generally relied upon a few beautiful analytical results or on numerical algorithms that combine heuristics with look-ahead approaches(α−βpruning). Using Chomp as a prototype,this new geometrical approach unveils unexpected parallels between combinatorial games and key ideas from physics and dynamical systems,most notably notions of scaling,renormalization,universality,and chaotic attractors.Their centralfinding is that underlying the game is a probabilistic geometric structure that encodes essential information about the game, and that this structure exhibits a type of scale invariance:Loosely speaking,the geometry of small winning positions and large winning positions are the same after rescaling.(This generalfinding also holds for at least some other combinatorial games,as was explicitly demonstrated with a variantof Nim.)This geometric insight not only provides(probabilistic)answers to some open questions about Chomp,but suggests a natural pathway toward a new class of algorithms for more general combinatorial games,and hints at deeper links between such games and nonlinear science.Chomp is an ideal candidate for the study,since in certain respects it appears to be among the simplest in the class of hard games.Its history is marked by some significant theoretical advances but it has yet to succumb to a complete analysis in the30years since its introduction by Gale and Schuh.The rules of Chomp are easily explained.Play begins with an N x M array of counters.On each turn a player selects a counter and removes it along with all counters to the north and east of it.Play alternates between the two players until one player takes the last counter,thereby losing the game.(An intriguing feature of Chomp,as shown by Gale,is that although it is very easy to prove that the player who movesfirst can always win,under optimal play,what this opening move should be has been an open question.The methodology provides a probabilistic answer to this question.) For simplicity,consider the case of three-row(M=3)Chomp,a subject of recent study by Zeil-berger[29]and Sun[26].Generalizations to four-row and higher Chomp are analogous.To start, note that the configuration of the counters at any stage of the game can be described(using Zeil-bergers coordinates)by the position p=[x,y,z],where x specifies the number of columns of height three,y specifies the number of columns of height two,and z the number with height one.Each position p may be classified as either a winner,if a player starting from that position can always force a win,or as a loser otherwise.The set of all losers contains the information for solving the game.One may conveniently group the losing positions according to their x values by defining a loser sheet Lx to be an infinite two-dimensional matrix whose(y,z)th component is a1if position [x,y,z]is a loser,and a0otherwise.(As noted by Zeilberger,one can express Lx in terms of all preceding loser sheets Lx-1,Lx-2,,L0.)Studies by Zeilberger[29,30]and others have detected several numerical patterns along with a few analytical features about the losing positions,and their interesting but non-obvious properties have even led to a conjecture that Chomp may be chaotic in a yet-to-be-made-precise sense.However,many of the numerical observations to date have remained largely unexplained,and disjoint from one another.To provide broader insight into the general structure of the game,the authors departed from the usual analytic/algebraic/algorithmic approaches.Instead showing how the analysis of the game can be recast and transformed into a type of renormalization problem commonly seen in physics (and later apply this methodology to other combinatorial games besides Chomp).Analysis of the resulting renormalization problem not only explains earlier numerical observations,but provides a unified,global description of the overall structure of the game.This approach will be distinguished by its decidedly geometricflavor,and by the incorporation of probabilistic elements into the analysis, despite the fact that the combinatorial games we consider are all games of no chance which lack any inherent probabilistic components to them whatsoever.To proceed,consider the so-called instant-winner sheets,defined as follows:A position p=[x,y,z] is called an instant winner if from that position a player can legally move to a losing position with a smaller x-value.We therefore define an instant-winner sheet Wx to be the infinite,two-dimensional matrix consisting of all instant winners with the specified x-value,i.e.,the(y,z)th component of matrix Wx is a1if position[x,y,z]is an instant winner,and a0otherwise.These instant-winner sheets will prove crucial for understanding the geometric structure of the game.Theirfirst insight comes from numerical simulations.They numerically construct the instant winner sheets Wx for various x values using a recursive algorithm.Each sheet exhibits a nontrivial internal structure characterized by several distinct regions:a solid(filled)triangular region at the lower left,a series of horizontal bands extending to the right(towards infinity),and two other triangular regions of different densities.Most importantly,however,we observe that the set of instant-winner sheets Wx possess a remarkable scaling property:their overall geometric shape is identical up to a scaling factor!In particular,as x increases,all boundary-line slopes,densities,and shapes of the various regions are preserved from one sheet to the next(although the actual point-by-point locations of the instant winners within each sheet are different).Hence,upon rescaling, the overall geometric structure of these sheets is identical(in a probabilistic sense).The growth (with increasing x)of the instant-winner sheets is strikingly similar to certain crystal-growth and aggregation processes found in physics in each case,the structures grow through the accumulationof new points along current boundaries,and exhibit geometric invariance during this process.The loser sheets Lx can be numerically constructed in a similar manner;their characteristic geometry is revealed.It is found to consist of three(diffuse)lines:a lower line of slope mL and density of points L,an upper line of slope mU and density U,and aflat line extending to infinity.The upper and lower lines originate from a point whose height(i.e.,z-value)is ax.Theflat line(with density one)is only present with probability in randomly selected loser sheets.Like the instant-winner sheets,the loser sheets also exhibit this remarkable geometric scaling property:as x increases,the geometric structure of Lx grows in size,but its overall shape remains unchanged(the only caveat being that, as previously noted,theflat line seen in is sometimes absent in some of the loser sheets).The second keyfinding is that there exists a well-defined,analytical recursion operator that relates one instant winner sheet to its immediate ly,one can write Wx+1=R Wx,where R denotes the recursion operator.(The operator R can be decomposed as R=L(I+DM), where L is a left-shift operator,I is the identity operator,D is a diagonal element-adding operator, and M is a sheet-valued version of the standard mex operator which is often used for combinatorial games.)They point out that once a given instant-winner sheet Wx has been constructed,the corresponding loser sheer Lx can be found via Lx=M Wx.The task is to determine an invariant geometric structure W such that if we act with the recursion operator followed by an appropriately-defined rescaling operator S,we get W back again:W=SR W (i.e.,find afixed point of the renormalization-group operator SR.)This can be done,but before doing so,even though the recursion operator R is exact and the game itself has absolutely no stochastic aspects to it,it is necessary to adopt a probabilistic framework in order to solve this recursion ly,the renormalization procedure will show that the slopes of all boundary lines and densities of all regions in the Wxs(and Lxs)are preserved not that there exists a point-by-point equivalence.In essence,bypassing consideration of the random-looking scatter of points surrounding the various lines and regions of Wx and Lx by effectively averaging over thesefluctuations.The key to implementing the renormalization analysis is to observe that the losers in Lx are constrained to lie along certain boundary lines of the Wx plot,and are conspicuously absent from the various interior regions of Wx(for all x).In other words,the interior regions of each Wx remain forbidden to the losers.Hence the geometry of Wxs must be very tightly constrained if it is to preserve these symmetries.References[1]D.T.Allemang.Generalized genus sequences for mis‘Internat.J.Game Theory,ereoctal games.30(4):539556(2002),2001.[2]Elwyn Berlekamp and David Wolfe.Mathematical Go:Chilling Gets the Last Point.A K Peters,Ltd.,Wellesley,Massachusetts,1994.[3]Elwyn R.Berlekamp,John H.Conway,and Richard K.Guy.Winning ways foryour mathematical plays.Vol.1.Academic Press Inc.[Harcourt Brace JovanovichPublishers],London,1982.Games in general.[4]Elwyn R.Berlekamp,John H.Conway,and Richard K.Guy.Winning ways for yourmathematical plays.Vol.1.A K Peters Ltd.,Natick,MA,second edition,2001.[5]C.L.Bouton.Nim,a game with a complete mathematical theory.Annals of Math-ematics,3(2):3539,1902.[6]J.H.Conway.On numbers and games.Academic Press[Harcourt Brace JovanovichPublishers],London,1976.London Mathematical Society Monographs,No.6.[7]J.H.Conway.Loopy games.Ann.Discrete Math.,3:5574,1978.Advances in graphtheory(Cambridge Combinatorial Conf.,Trinity College,Cambridge,1977).。