In Cognitive Robotics 1998 AAAI Fall Symposium

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机器人技术二级理论考点

目录二级理论知识点 (1)一．机器人常识 (1)1. 机器人历史事件及重要理论 (1)2. 机器人的产生 (2)1.机器人不应伤害人类； (2)3. 机器人的三代发展 (3)二、力的基础认知 (3)1. 力的效果 (3)2. 力的三要素 (3)3. 常见力的介绍 (3)(1) 重力 (3)(2) 摩擦力 (3)三、常用传动装置 (4)1. 齿轮啮合 (4)2. 皮带传动 (4)3. 传动链 (4)四、常用机械机构 (7)1.凸轮机构 (7)2. 连杆机构 (7)3. 曲柄机构 (8)(1) 曲柄滑块机构 (8)(2) 曲柄摇杆机构 (8)4.滑杆机构 (8)5.棘轮机构 (8)6间歇运动机构 (8)不完全齿轮机构中不完全齿轮为主动件 (9)7 常用机构示例图 (9)五电学 (19)1节干电池的电压为1.5V (19)六其它 (19)二级理论知识点一．机器人常识1. 机器人历史事件及重要理论机器马车。

西周时期，中国的能工巧匠偃师用动物皮、木头、树脂制出了能歌善舞的伶人，这是中国最早记载的木头机器人雏形。

汉代，大科学家张衡不仅发明了地动仪，而且发明了计里鼓车。

计里鼓车每行一里，车上木人击木马车鼓一下，每行十里击钟一下。

后汉三国时期，蜀国丞相诸葛亮成功地创造出了“木牛流马”，并用其运送军粮，支援前方战争。

2. 机器人的产生1920年，捷克斯洛伐克作家卡雷尔·恰佩克在他的科幻小说《罗萨姆的万能机器人》中，根据Robota和Robotnik两个单词，创造出了“Robot”机器人这个词。

从此之后机器人在历史舞台上拉开了序幕。

1942年，美国的西莫夫在的科幻小说中提出“机器人三大定律”：1.机器人不应伤害人类；2.机器人应遵守人类的命令，与第一条违背的命令除外；3.机器人应能保护自己，与前两条条相抵触者除外。

这是给机器人赋予的伦理性纲领。

机器人学术界一直将这三原则作为机器人开发的准则。

1954年美国人乔治德沃尔制造出世界上第一台可编程的机器人并注册了专利。

马文明斯基

马文 ·明斯基
“人工智能之父”、框架理论的创立者
01 人物生平
03 典型事例 05 人物评价
目录
02 主要成就 04 主要著作
马文 ·明斯基（ 1 9 2 7 年 8 月 9 日 - 2 0 1 6 年 1 月 2 4 日），男， “ 人工智能之父 ” 和框架理论的创立者。 1 9 5 6 年，和麦卡锡(J．McCarthy)一起发起“达特茅斯会议”并提出人工智能(Artificial Intelligence)概念的计算机科学家马文·明斯基(Marvin Lee Minsky)被授予了1969年度图灵奖，是第一位获此殊荣的人工智能学者。其后，麦卡锡 ( 1 9 7 1 年 ) ，西蒙 ( H ． A ． S i m o n ) 和纽厄尔 ( A ． N e w e l l . 1 9 7 5 年 ) ，费根鲍姆 ( E ． A ． F e i g e n b a u m ) 和劳伊 ·雷迪 (Raj Reddy,1994年)等5名人工智能学者先后获奖，在至2021年9月获图奖的40名学者中占了近1/6，可见人工智能学科影响之深远。明斯基的代表作包括《情感机器》《心智社会》等著作。
典型事例
例如，有一个关于汽车的框架如下： name：汽车 super-class：交通工具 sub-class：轿车，面包车，吉普车车轮个数： value-class：整数 default：4 value：未知车身长度： value-class：浮点数 ......
主要著作
情感机器《计算：有限与无限的机器》（Computation：Finite and Infinite Machines，Prentice-Hall， 1967)《语义信息处理》（Semantic Information Processing，MIT Pr.,1968)

Insight Problem Solving A Critical Examination of the Possibility

The Journal of Problem Solving • volume 5, no. 1 (Fall 2012)56Insight Problem Solving: A Critical Examination of the Possibilityof Formal TheoryWilliam H. Batchelder 1 and Gregory E. Alexander 1AbstractThis paper provides a critical examination of the current state and future possibility of formal cognitive theory for insight problem solving and its associated “aha!” experience. Insight problems are contrasted with move problems, which have been formally defined and studied extensively by cognitive psychologists since the pioneering work of Alan Newell and Herbert Simon. To facilitate our discussion, a number of classical brainteasers are presented along with their solutions and some conclusions derived from observing the behavior of many students trying to solve them. Some of these problems are interesting in their own right, and many of them have not been discussed before in the psychologi-cal literature. The main purpose of presenting the brainteasers is to assist in discussing the status of formal cognitive theory for insight problem solving, which is argued to be considerably weaker than that found in other areas of higher cognition such as human memory, decision-making, categorization, and perception. We discuss theoretical barri-ers that have plagued the development of successful formal theory for insight problem solving. A few suggestions are made that might serve to advance the field.Keywords Insight problems, move problems, modularity, problem representation1 Department of Cognitive Sciences, University of California Irvine/10.7771/1932-6246.1143Insight Problem Solving: The Possibility of Formal Theory 57• volume 5, no. 1 (Fall 2012)1. IntroductionThis paper discusses the current state and a possible future of formal cognitive theory for insight problem solving and its associated “aha!” experience. Insight problems are con-trasted with so-called move problems defined and studied extensively by Alan Newell and Herbert Simon (1972). These authors provided a formal, computational theory for such problems called the General Problem Solver (GPS), and this theory was one of the first formal information processing theories to be developed in cognitive psychology. A move problem is posed to solvers in terms of a clearly defined representation consisting of a starting state, a description of the goal state(s), and operators that allow transitions from one problem state to another, as in Newell and Simon (1972) and Mayer (1992). A solu-tion to a move problem involves applying operators successively to generate a sequence of transitions (moves) from the starting state through intermediate problem states and finally to a goal state. Move problems will be discussed more extensively in Section 4.6.In solving move problems, insight may be required for selecting productive moves at various states in the problem space; however, for our purposes we are interested in the sorts of problems that are described often as insight problems. Unlike Newell and Simon’s formal definition of move problems, there has not been a generally agreed upon defini-tion of an insight problem (Ash, Jee, and Wiley, 2012; Chronicle, MacGregor, and Ormerod, 2004; Chu and MacGregor, 2011). It is our view that it is not productive to attempt a pre-cise logical definition of an insight problem, and instead we offer a set of shared defining characteristics in the spirit of Wittgenstein’s (1958) definition of ‘game’ in terms of family resemblances. Problems that we will treat as insight problems share many of the follow-ing defining characteristics: (1) They are posed in such a way as to admit several possible problem representations, each with an associated solution search space. (2) Likely initial representations are inadequate in that they fail to allow the possibility of discovering a problem solution. (3) In order to overcome such a failure, it is necessary to find an alternative productive representation of the problem. (4) Finding a productive problem representation may be facilitated by a period of non-solving activity called incubation, and also it may be potentiated by well-chosen hints. (5) Once obtained, a productive representation leads quite directly and quickly to a solution. (6) The solution involves the use of knowledge that is well known to the solver. (7) Once the solution is obtained, it is accompanied by a so-called “aha!” experience. (8) When a solution is revealed to a non-solver, it is grasped quickly, often with a feeling of surprise at its simplicity, akin to an “aha!” experience.It is our position that very little is known empirically or theoretically about the cogni-tive processes involved in solving insight problems. Furthermore, this lack of knowledge stands in stark contrast with other areas of cognition such as human memory, decision-making, categorization, and perception. These areas of cognition have a large number of replicable empirical facts, and many formal theories and computational models exist that attempt to explain these facts in terms of underlying cognitive processes. The main goal58W. H. Batchelder and G. E. Alexander of this paper is to explain the reasons why it has been so difficult to achieve a scientific understanding of the cognitive processes involved in insight problem solving.There have been many scientific books and papers on insight problem solving, start-ing with the seminal work of the Gestalt psychologists Köhler (1925), Duncker (1945), and Wertheimer (1954), as well as the English social psychologist, Wallas (1926). Since the contributions of the early Gestalt psychologists, there have been many journal articles, a few scientific books, such as those by Sternberg and Davidson (1996) and Chu (2009), and a large number of books on the subject by laypersons. Most recently, two excellent critical reviews of insight problem solving have appeared: Ash, Cushen, and Wiley (2009) and Chu and MacGregor (2011).The approach in this paper is to discuss, at a general level, the nature of several fun-damental barriers to the scientific study of insight problem solving. Rather than criticizing particular experimental studies or specific theories in detail, we try to step back and take a look at the area itself. In this effort, we attempt to identify principled reasons why the area of insight problem solving is so resistant to scientific progress. To assist in this approach we discuss and informally analyze eighteen classical brainteasers in the main sections of the paper. These problems are among many that have been posed to hundreds of upper divisional undergraduate students in a course titled “Human Problem Solving” taught for many years by the senior author. Only the first two of these problems can be regarded strictly as move problems in the sense of Newell and Simon, and most of the rest share many of the characteristics of insight problems as described earlier.The paper is divided into five main sections. After the Introduction, Section 2 describes the nature of the problem solving class. Section 3 poses the eighteen brainteasers that will be discussed in later sections of the paper. The reader is invited to try to solve these problems before checking out the solutions in the Appendix. Section 4 lays out six major barriers to developing a deep scientific theory of insight problem solving that we believe are endemic to the field. We argue that these barriers are not present in other, more theo-retically advanced areas of higher cognition such as human memory, decision-making, categorization, and perception. These barriers include the lack of many experimental paradigms (4.1), the lack of a large, well-classified set of stimulus material (4.2), and the lack of many informative behavioral measures (4.3). In addition, it is argued that insight problem solving is difficult to study because it is non-modular, both in the sense of Fodor (1983) but more importantly in several weaker senses of modularity that admit other areas of higher cognition (4.4), the lack of theoretical generalizations about insight problem solv-ing from experiments with particular insight problems (4.5), and the lack of computational theories of human insight (4.6). Finally, in Section 5, we suggest several avenues that may help overcome some of the barriers described in Section 4. These include suggestions for useful classes of insight problems (5.1), suggestions for experimental work with expert problem solvers (5.2), and some possibilities for a computational theory of insight.The Journal of Problem Solving •Insight Problem Solving: The Possibility of Formal Theory 592. Batchelder’s Human Problem Solving ClassThe senior author, William Batchelder, has taught an Upper Divisional Undergraduate course called ‘Human Problem Solving” for over twenty-five years to classes ranging in size from 75 to 100 students. By way of background, his active research is in other areas of the cognitive sciences; however, he maintains a long-term hobby of studying classical brainteasers. In the area of complex games, he achieved the title of Senior Master from the United States Chess Federation, he was an active duplicate bridge player throughout undergraduate and graduate school, and he also achieved a reasonable level of skill in the game of Go.The content of the problem-solving course is split into two main topics. The first topic involves encouraging students to try their hand at solving a number of famous brainteasers drawn from the sizeable folklore of insight problems, especially the work of Martin Gardner (1978, 1982), Sam Loyd (1914), and Raymond Smullyan (1978). In addition, games like chess, bridge, and Go are discussed. The second topic involves presenting the psychological theory of thinking and problem solving, and in most cases the material is organized around developments in topics that are covered in the first eight chapters of Mayer (1992). These topics include work of the Gestalt psychologists on problem solving, discussion of experiments and theories concerning induction and deduction, present-ing the work on move problems, including the General Problem Solver (Newell & Simon, 1972), showing how response time studies can reveal mental architectures, and describing theories of memory representation and question answering.Despite efforts, the structure of the course does not reflect a close overlap between its two main topics. The principal reason for this is that in our view the level of theoreti-cal and empirical work on insight problem solving is at a substantially lower level than is the work in almost any other area of cognition dealing with higher processes. The main goal of this paper is to explain our reasons for this pessimistic view. To assist in this goal, it is helpful to get some classical brainteasers on the table. While most of these problems have not been used in experimental studies, the senior author has experienced the solu-tion efforts and post solution discussions of over 2,000 students who have grappled with these problems in class.3. Some Classic BrainteasersIn this section we present eighteen classical brainteasers from the folklore of problem solving that will be discussed in the remainder of the paper. These problems have de-lighted brainteaser connoisseurs for years, and most are capable of giving the solver a large dose of the “aha!” experience. There are numerous collections of these problems in books, and many collections of them are accessible through the Internet. We have selected these problems because they, and others like them, pose a real challenge to any effort to • volume 5, no. 1 (Fall 2012)60W. H. Batchelder and G. E. Alexander develop a deep and general formal theory of human or machine insight problem solving. With the exception of Problems 3.1 and 3.2, and arguably 3.6, the problems are different in important respects from so-called move problems of Newell and Simon (1972) described earlier and in Section 4.6.Most of the problems posed in this section share many of the defining characteristics of insight problems described in Section 1. In particular, they do not involve multiple steps, they require at most a very minimal amount of technical knowledge, and most of them can be solved by one or two fairly simple insights, albeit insights that are rarely achieved in real time by problem solvers. What makes these problems interesting is that they are posed in such a way as to induce solvers to represent the problem information in an unproductive way. Then the main barrier to finding a solution to one of these problems is to overcome a poor initial problem representation. This may involve such things as a re-representation of the problem, the dropping of an implicit constraint on the solution space, or seeing a parallel to some other similar problem. If the solver finds a productive way of viewing the problem, the solution generally follows rapidly and comes with burst of insight, namely the “aha!” experience. In addition, when non-solvers are given the solu-tion they too may experience a burst of insight.What follows next are statements of the eighteen brainteasers. The solutions are presented in the Appendix, and we recommend that after whatever problem solving activity a reader wishes to engage in, that the Appendix is studied before reading the remaining two sections of the paper. As we discuss each problem in the paper, we provide authorship information where authorship is known. In addition, we rephrased some of the problems from their original sources.Problem 3.1. Imagine you have an 8-inch by 8-inch array of 1-inch by 1-inch little squares. You also have a large box of 2-inch by 1-inch rectangular shaped dominoes. Of course it is easy to tile the 64 little squares with dominoes in the sense that every square is covered exactly once by a domino and no domino is hanging off the array. Now sup-pose the upper right and lower left corner squares are cut off the array. Is it possible to tile the new configuration of 62 little squares with dominoes allowing no overlaps and no overhangs?Problem 3.2. A 3-inch by 3-inch by 3-inch cheese cube is made of 27 little 1-inch cheese cubes of different flavors so that it is configured like a Rubik’s cube. A cheese-eating worm devours one of the top corner cubes. After eating any little cube, the worm can go on to eat any adjacent little cube (one that shares a wall). The middlemost little cube is by far the tastiest, so our worm wants to eat through all the little cubes finishing last with the middlemost cube. Is it possible for the worm to accomplish this goal? Could he start with eating any other little cube and finish last with the middlemost cube as the 27th?The Journal of Problem Solving •Insight Problem Solving: The Possibility of Formal Theory 61 Figure 1. The cheese eating worm problem.Problem 3.3. You have ten volumes of an encyclopedia numbered 1, . . . ,10 and shelved in a bookcase in sequence in the ordinary way. Each volume has 100 pages, and to simplify suppose the front cover of each volume is page 1 and numbering is consecutive through page 100, which is the back cover. You go to sleep and in the middle of the night a bookworm crawls onto the bookcase. It eats through the first page of the first volume and eats continuously onwards, stopping after eating the last page of the tenth volume. How many pieces of paper did the bookworm eat through?Figure 2.Bookcase setup for the Bookworm Problem.Problem 3.4. Suppose the earth is a perfect sphere, and an angel fits a tight gold belt around the equator so there is no room to slip anything under the belt. The angel has second thoughts and adds an inch to the belt, and fits it evenly around the equator. Could you slip a dime under the belt?• volume 5, no. 1 (Fall 2012)62W. H. Batchelder and G. E. Alexander Problem 3.5. Consider the cube in Figure 1 and suppose the top and bottom surfaces are painted red and the other four sides are painted blue. How many little cubes have at least one red and at least one blue side?Problem 3.6. Look at the nine dots in Figure 3. Your job is to take a pencil and con-nect them using only three straight lines. Retracing a line is not allowed and removing your pencil from the paper as you draw is not allowed. Note the usual nine-dot problem requires you to do it with four lines; you may want to try that stipulation as well. Figure 3.The setup for the Nine-Dot Problem.Problem 3.7. You are standing outside a light-tight, well-insulated closet with one door, which is closed. The closet contains three light sockets each containing a working light bulb. Outside the closet, there are three on/off light switches, each of which controls a different one of the sockets in the closet. All switches are off. Your task is to identify which switch operates which light bulb. You can turn the switches off and on and leave them in any position, but once you open the closet door you cannot change the setting of any switch. Your task is to figure out which switch controls which light bulb while you are only allowed to open the door once.Figure 4.The setup of the Light Bulb Problem.The Journal of Problem Solving •Insight Problem Solving: The Possibility of Formal Theory 63• volume 5, no . 1 (Fall 2012)Problem 3.8. We know that any finite string of symbols can be extended in infinitely many ways depending on the inductive (recursive) rule; however, many of these ways are not ‘reasonable’ from a human perspective. With this in mind, find a reasonable rule to continue the following series:Problem 3.9. You have two quart-size beakers labeled A and B. Beaker A has a pint of coffee in it and beaker B has a pint of cream in it. First you take a tablespoon of coffee from A and pour it in B. After mixing the contents of B thoroughly you take a tablespoon of the mixture in B and pour it back into A, again mixing thoroughly. After the two transfers, which beaker, if either, has a less diluted (more pure) content of its original substance - coffee in A or cream in B? (Forget any issues of chemistry such as miscibility).Figure 5. The setup of the Coffee and Cream Problem.Problem 3.10. There are two large jars, A and B. Jar A is filled with a large number of blue beads, and Jar B is filled with the same number of red beads. Five beads from Jar A are scooped out and transferred to Jar B. Someone then puts a hand in Jar B and randomly grabs five beads from it and places them in Jar A. Under what conditions after the second transfer would there be the same number of red beads in Jar A as there are blue beads in Jar B.Problem 3.11. Two trains A and B leave their train stations at exactly the same time, and, unaware of each other, head toward each other on a straight 100-mile track between the two stations. Each is going exactly 50 mph, and they are destined to crash. At the time the trains leave their stations, a SUPERFLY takes off from the engine of train A and flies directly toward train B at 100 mph. When he reaches train B, he turns around instantly, A BCD EF G HI JKLM.............64W. H. Batchelder and G. E. Alexander continuing at 100 mph toward train A. The SUPERFLY continues in this way until the trains crash head-on, and on the very last moment he slips out to live another day. How many miles does the SUPERFLY travel on his zigzag route by the time the trains collide?Problem 3.12. George lives at the foot of a mountain, and there is a single narrow trail from his house to a campsite on the top of the mountain. At exactly 6 a.m. on Satur-day he starts up the trail, and without stopping or backtracking arrives at the top before6 p.m. He pitches his tent, stays the night, and the next morning, on Sunday, at exactly 6a.m., he starts down the trail, hiking continuously without backtracking, and reaches his house before 6 p.m. Must there be a time of day on Sunday where he was exactly at the same place on the trail as he was at that time on Saturday? Could there be more than one such place?Problem 3.13. You are driving up and down a mountain that is 20 miles up and 20 miles down. You average 30 mph going up; how fast would you have to go coming down the mountain to average 60 mph for the entire trip?Problem 3.14. During a recent census, a man told the census taker that he had three children. The census taker said that he needed to know their ages, and the man replied that the product of their ages was 36. The census taker, slightly miffed, said he needed to know each of their ages. The man said, “Well the sum of their ages is the same as my house number.” The census taker looked at the house number and complained, “I still can’t tell their ages.” The man said, “Oh, that’s right, the oldest one taught the younger ones to play chess.” The census taker promptly wrote down the ages of the three children. How did he know, and what were the ages?Problem 3.15. A closet has two red hats and three white hats. Three participants and a Gamesmaster know that these are the only hats in play. Man A has two good eyes, man B only one good eye, and man C is blind. The three men sit on chairs facing each other, and the Gamesmaster places a hat on each man’s head, in such a way that no man can see the color of his own hat. The Gamesmaster offers a deal, namely if any man correctly states the color of his hat, he will get $50,000; however, if he is in error, then he has to serve the rest of his life as an indentured servant to the Gamesmaster. Man A looks around and says, “I am not going to guess.” Then Man B looks around and says, “I am not going to guess.” Finally Man C says, “ From what my friends with eyes have said, I can clearly see that my hat is _____”. He wins the $50,000, and your task is to fill in the blank and explain how the blind man knew the color of his hat.Problem 3.16. A king dies and leaves an estate, including 17 horses, to his three daughters. According to his will, everything is to be divided among his daughters as fol-lows: 1/2 to the oldest daughter, 1/3 to the middle daughter, and 1/9 to the youngest daughter. The three heirs are puzzled as to how to divide the horses among themselves, when a probate lawyer rides up on his horse and offers to assist. He adds his horse to the kings’ horses, so there will be 18 horses. Then he proceeds to divide the horses amongThe Journal of Problem Solving •Insight Problem Solving: The Possibility of Formal Theory 65 the daughters. The oldest gets ½ of the horses, which is 9; the middle daughter gets 6 horses which is 1/3rd of the horses, and the youngest gets 2 horses, 1/9th of the lot. That’s 17 horses, so the lawyer gets on his own horse and rides off with a nice commission. How was it possible for the lawyer to solve the heirs’ problem and still retain his own horse?Problem 3.17. A logical wizard offers you the opportunity to make one statement: if it is false, he will give you exactly ten dollars, and if it is true, he will give you an amount of money other than ten dollars. Give an example of a statement that would be sure to make you rich.Problem 3.18. Discover an interesting sense of the claim that it is in principle impos-sible to draw a perfect map of England while standing in a London flat; however, it is not in principle impossible to do so while living in a New York City Pad.4. Barriers to a Theory of Insight Problem SolvingAs mentioned earlier, our view is that there are a number of theoretical barriers that make it difficult to develop a satisfactory formal theory of the cognitive processes in play when humans solve classical brainteasers of the sort posed in Section 3. Further these barriers seem almost unique to insight problem solving in comparison with the more fully developed higher process areas of the cognitive sciences such as human memory, decision-making, categorization, and perception. Indeed it seems uncontroversial to us that neither human nor machine insight problem solving is well understood, and com-pared to other higher process areas in psychology, it is the least developed area both empirically and theoretically.There are two recent comprehensive critical reviews concerning insight problem solving by Ash, Cushen, and Wiley (2009) and Chu and MacGregor (2011). These articles describe the current state of empirical and theoretical work on insight problem solving, with a focus on experimental studies and theories of problem restructuring. In our view, both reviews are consistent with our belief that there has been very little sustainable progress in achieving a general scientific understanding of insight. Particularly striking is that are no established general, formal theories or models of insight problem solving. By a general formal model of insight problem solving we mean a set of clearly formulated assumptions that lead formally or logically to precise behavioral predictions over a wide range of insight problems. Such a formal model could be posed in terms of a number of formal languages including information processing assumptions, neural networks, computer simulation, stochastic assumptions, or Bayesian assumptions.Since the groundbreaking work by the Gestalt psychologists on insight problem solving, there have been theoretical ideas that have been helpful in explaining the cog-nitive processes at play in solving certain selected insight problems. Among the earlier ideas are Luchins’ concept of einstellung (blind spot) and Duncker’s functional fixedness, • volume 5, no. 1 (Fall 2012)as in Maher (1992). More recently, there have been two developed theoretical ideas: (1) Criterion for Satisfactory Progress theory (Chu, Dewald, & Chronicle, 2007; MacGregor, Ormerod, & Chronicle, 2001), and (2) Representational Change Theory (Knoblich, Ohls-son, Haider, & Rhenius, 1999). We will discuss these theories in more detail in Section 4. While it is arguable that these theoretical ideas have done good work in understanding in detail a few selected insight problems, we argue that it is not at all clear how these ideas can be generalized to constitute a formal theory of insight problem solving at anywhere near the level of generality that has been achieved by formal theories in other areas of higher process cognition.The dearth of formal theories of insight problem solving is in stark contrast with other areas of problem solving discussed in Section 4.6, for example move problems discussed earlier and the more recent work on combinatorial optimization problems such as the two dimensional traveling salesman problem (MacGregor and Chu, 2011). In addition, most other higher process areas of cognition are replete with a variety of formal theories and models. For example, in the area of human memory there are currently a very large number of formal, information processing models, many of which have evolved from earlier mathematical models, as in Norman (1970). In the area of categorization, there are currently several major formal theories along with many variations that stem from earlier theories discussed in Ashby (1992) and Estes (1996). In areas ranging from psycholinguistics to perception, there are a number of formal models based on brain-style computation stemming from Rumelhart, McClelland, and PDP Research Group’s (1987) classic two-volume book on parallel distributed processing. Since Daniel Kahneman’s 2002 Nobel Memorial Prize in the Economic Sciences for work jointly with Amos Tversky developing prospect theory, as in Kahneman and Tversky (1979), psychologically based formal models of human decision-making is a major theoretical area in cognitive psychology today. In our view, there is nothing in the area of insight problem solving that approaches the depth and breadth of formal models seen in the areas mentioned above.In the following subsections, we will discuss some of the barriers that have prevented the development of a satisfactory theory of insight problem solving. Some of the bar-riers will be illustrated with references to the problems in Section 3. Then, in Section 5 we will assuage our pessimism a bit by suggesting how some of these barriers might be removed in future work to facilitate the development of an adequate theory of insight problem solving.4.1 Lack of Many Experimental ParadigmsThere are not many distinct experimental paradigms to study insight problem solving. The standard paradigm is to pick a particular problem, such as one of the ones in Section 3, and present it to several groups of subjects, perhaps in different ways. For example, groups may differ in the way a hint is presented, a diagram is provided, or an instruction。

人工智能训练师培训教材和考试题库

人工智能训练师培训教材和考试题库一、介绍在当今信息时代，人工智能技术正在不断发展和应用，越来越多的人对人工智能感兴趣并希望学习和掌握相关知识。

在这样的背景下，人工智能训练师培训教材和考试题库变得尤为重要。

本文将对人工智能训练师培训教材和考试题库进行全面评估，并撰写有关内容，以引导读者深入理解和应用人工智能相关知识。

二、教材内容评估1. 剖析人工智能概念人工智能训练师培训教材应当深入浅出地剖析人工智能的概念，包括定义、发展历程、基本原理等内容。

这有助于培训者建立对人工智能的全面理解，为后续学习打下坚实基础。

2. 介绍人工智能技术与应用教材还应包括对人工智能技术与应用领域的介绍，如机器学习、深度学习、自然语言处理、计算机视觉、智能交互系统等，以便培训者了解人工智能的具体应用场景和实践技术。

3. 探讨人工智能伦理与法律由于人工智能技术涉及到伦理和法律问题，教材还应包括对人工智能伦理与法律的探讨，培训者需了解在人工智能应用中要遵守的道德规范和法律法规，以确保人工智能的合理、安全和良好应用。

4. 结合案例分析为了帮助培训者更好地理解和应用人工智能知识，教材应当结合案例分析，具体剖析人工智能在不同领域的实际应用，并对其技术和道德问题进行深入讨论。

5. 知识强化与拓展教材还应包括知识强化与拓展，包括练习题、案例分析、课程设计等内容，以便培训者进行深入学习和巩固。

三、考试题库设计评估1. 考察基础知识人工智能训练师考试题库应当覆盖人工智能的基础知识，包括定义、发展、核心技术、伦理与法律等内容，以考察考生对人工智能基本概念的掌握程度。

2. 考察应用能力考试题库还应包括涉及人工智能技术与应用的题目，如实际案例分析、解决问题的能力等，以考察考生运用人工智能知识解决实际问题的能力。

3. 考察伦理与法律意识由于人工智能涉及到伦理与法律问题，考试题库还应包括考察考生对人工智能伦理与法律的理解和应用能力的题目，如道德难题、法律规范适用等。

计算机领域会议排名

计算机领域国际会议分类排名现在的会议非常多，在投文章前，大家可以先看看会议的权威性、前几届的录用率，这样首先对自己的文章能不能中有个大概的心理底线。

权威与否可以和同行的同学沟通、或者看录用文章的水平、或者自己平时阅读文献的时候的慢慢累及。

原来有人做过一个国际会议的排名，如下.sg/home/assourav/crank.htm其中的很多会议我们都非常熟悉的。

但是这个排名是大概2000的时候做的，后来没有更新，所以像ISWC 这个会议在其中就看不到。

但是很多悠久的会议上面都有的，如www,SIGIR,VLDB,EMLC,ICTAI这些等等。

这些东西可以作为一个参考。

现在很多学校的同学毕业都要有检索的要求了。

因此很多不在SCI，EI检索范围内的会议投了可能对毕业无用，所以投之前最好查查会议是不是被SCI，EI检索的。

当然这也不绝对，如Web领域最权威的WWW的全文就只是ISTP检索，而不是SCI，EI检索的（可能是ACM出版的原因吧？）。

罗嗦了这么多！祝愿大家能在好的会议上发PAPER，能被SCI，EI检索。

---------------附，会议排名（from .sg/home/assourav/crank.htm）Computer Science Conference RankingsSome conferences accept multiple categories of papers. The rankings below are for the mos t prestigious category of paper at a given conference. All other categories should be treat ed as "unranked".AREA: DatabasesRank 1:SIGMOD: ACM SIGMOD Conf on Management of DataPODS: ACM SIGMOD Conf on Principles of DB SystemsVLDB: Very Large Data BasesICDE: Intl Conf on Data EngineeringICDT: Intl Conf on Database TheoryRank 2:SSD: Intl Symp on Large Spatial DatabasesDEXA: Database and Expert System ApplicationsFODO: Intl Conf on Foundation on Data OrganizationEDBT: Extending DB TechnologyDOOD: Deductive and Object-Oriented DatabasesDASFAA: Database Systems for Advanced ApplicationsCIKM: Intl. Conf on Information and Knowledge ManagementSSDBM: Intl Conf on Scientific and Statistical DB MgmtCoopIS - Conference on Cooperative Information SystemsER - Intl Conf on Conceptual Modeling (ER)Rank 3:COMAD: Intl Conf on Management of DataBNCOD: British National Conference on DatabasesADC: Australasian Database ConferenceADBIS: Symposium on Advances in DB and Information SystemsDaWaK - Data Warehousing and Knowledge DiscoveryRIDE WorkshopIFIP-DS: IFIP-DS ConferenceIFIP-DBSEC - IFIP Workshop on Database SecurityNGDB: Intl Symp on Next Generation DB Systems and AppsADTI: Intl Symp on Advanced DB Technologies and Integration FEWFDB: Far East Workshop on Future DB SystemsMDM - Int. Conf. on Mobile Data Access/Management (MDA/MDM)ICDM - IEEE International Conference on Data MiningVDB - Visual Database SystemsIDEAS - International Database Engineering and Application Symposium Others:ARTDB - Active and Real-Time Database SystemsCODAS: Intl Symp on Cooperative DB Systems for Adv AppsDBPL - Workshop on Database Programming LanguagesEFIS/EFDBS - Engineering Federated Information (Database) Systems KRDB - Knowledge Representation Meets DatabasesNDB - National Database Conference (China)NLDB - Applications of Natural Language to Data BasesFQAS - Flexible Query-Answering SystemsIDC(W) - International Database Conference (HK CS)RTDB - Workshop on Real-Time DatabasesSBBD: Brazilian Symposium on DatabasesWebDB - International Workshop on the Web and DatabasesWAIM: Interational Conference on Web Age Information ManagementDASWIS - Data Semantics in Web Information SystemsDMDW - Design and Management of Data WarehousesDOLAP - International Workshop on Data Warehousing and OLAPDMKD - Workshop on Research Issues in Data Mining and Knowledge DiscoveryKDEX - Knowledge and Data Engineering Exchange WorkshopNRDM - Workshop on Network-Related Data ManagementMobiDE - Workshop on Data Engineering for Wireless and Mobile AccessMDDS - Mobility in Databases and Distributed SystemsMEWS - Mining for Enhanced Web SearchTAKMA - Theory and Applications of Knowledge MAnagementWIDM: International Workshop on Web Information and Data ManagementW2GIS - International Workshop on Web and Wireless Geographical Information Systems CDB - Constraint Databases and ApplicationsDTVE - Workshop on Database Technology for Virtual EnterprisesIWDOM - International Workshop on Distributed Object ManagementOODBS - Workshop on Object-Oriented Database SystemsPDIS: Parallel and Distributed Information SystemsAREA: Artificial Intelligence and Related SubjectsRank 1:AAAI: American Association for AI National ConferenceCVPR: IEEE Conf on Comp Vision and Pattern RecognitionIJCAI: Intl Joint Conf on AIICCV: Intl Conf on Computer VisionICML: Intl Conf on Machine LearningKDD: Knowledge Discovery and Data MiningKR: Intl Conf on Principles of KR & ReasoningNIPS: Neural Information Processing SystemsUAI: Conference on Uncertainty in AIAAMAS: Intl Conf on Autonomous Agents and Multi-Agent Systems (past: ICAA)ACL: Annual Meeting of the ACL (Association of Computational Linguistics)Rank 2:NAACL: North American Chapter of the ACLAID: Intl Conf on AI in DesignAI-ED: World Conference on AI in EducationCAIP: Inttl Conf on Comp. Analysis of Images and PatternsCSSAC: Cognitive Science Society Annual ConferenceECCV: European Conference on Computer VisionEAI: European Conf on AIEML: European Conf on Machine LearningGECCO: Genetic and Evolutionary Computation Conference (used to be GP)IAAI: Innovative Applications in AIICIP: Intl Conf on Image ProcessingICNN/IJCNN: Intl (Joint) Conference on Neural NetworksICPR: Intl Conf on Pattern RecognitionICDAR: International Conference on Document Analysis and RecognitionICTAI: IEEE conference on Tools with AIAMAI: Artificial Intelligence and MathsDAS: International Workshop on Document Analysis SystemsWACV: IEEE Workshop on Apps of Computer VisionCOLING: International Conference on Computational LiguisticsEMNLP: Empirical Methods in Natural Language ProcessingEACL: Annual Meeting of European Association Computational LingusticsCoNLL: Conference on Natural Language LearningDocEng: ACM Symposium on Document EngineeringIEEE/WIC International Joint Conf on Web Intelligence and Intelligent Agent Technology Rank 3:PRICAI: Pacific Rim Intl Conf on AIAAI: Australian National Conf on AIACCV: Asian Conference on Computer VisionAI*IA: Congress of the Italian Assoc for AIANNIE: Artificial Neural Networks in EngineeringANZIIS: Australian/NZ Conf on Intelligent Inf. SystemsCAIA: Conf on AI for ApplicationsCAAI: Canadian Artificial Intelligence ConferenceASADM: Chicago ASA Data Mining Conf: A Hard Look at DMEPIA: Portuguese Conference on Artificial IntelligenceFCKAML: French Conf on Know. Acquisition & Machine LearningICANN: International Conf on Artificial Neural NetworksICCB: International Conference on Case-Based ReasoningICGA: International Conference on Genetic AlgorithmsICONIP: Intl Conf on Neural Information ProcessingIEA/AIE: Intl Conf on Ind. & Eng. Apps of AI & Expert SysICMS: International Conference on Multiagent SystemsICPS: International conference on Planning SystemsIWANN: Intl Work-Conf on Art & Natural Neural NetworksPACES: Pacific Asian Conference on Expert SystemsSCAI: Scandinavian Conference on Artifical IntelligenceSPICIS: Singapore Intl Conf on Intelligent SystemPAKDD: Pacific-Asia Conf on Know. Discovery & Data MiningSMC: IEEE Intl Conf on Systems, Man and CyberneticsPAKDDM: Practical App of Knowledge Discovery & Data MiningWCNN: The World Congress on Neural NetworksWCES: World Congress on Expert SystemsASC: Intl Conf on AI and Soft ComputingPACLIC: Pacific Asia Conference on Language, Information and ComputationICCC: International Conference on Chinese ComputingICADL: International Conference on Asian Digital LibrariesRANLP: Recent Advances in Natural Language ProcessingNLPRS: Natural Language Pacific Rim SymposiumMeta-Heuristics International ConferenceRank 3:ICRA: IEEE Intl Conf on Robotics and AutomationNNSP: Neural Networks for Signal ProcessingICASSP: IEEE Intl Conf on Acoustics, Speech and SPGCCCE: Global Chinese Conference on Computers in EducationICAI: Intl Conf on Artificial IntelligenceAEN: IASTED Intl Conf on AI, Exp Sys & Neural NetworksWMSCI: World Multiconfs on Sys, Cybernetics & InformaticsLREC: Language Resources and Evaluation ConferenceAIMSA: Artificial Intelligence: Methodology, Systems, ApplicationsAISC: Artificial Intelligence and Symbolic ComputationCIA: Cooperative Information AgentsInternational Conference on Computational Intelligence for Modelling, Control and Automation Pattern MatchingECAL: European Conference on Artificial LifeEKAW: Knowledge Acquisition, Modeling and ManagementEMMCVPR: Energy Minimization Methods in Computer Vision and Pattern RecognitionEuroGP: European Conference on Genetic ProgrammingFoIKS: Foundations of Information and Knowledge SystemsIAWTIC: International Conference on Intelligent Agents, Web Technologies and Internet Commer ceICAIL: International Conference on Artificial Intelligence and LawSMIS: International Syposium on Methodologies for Intelligent SystemsIS&N: Intelligence and Services in NetworksJELIA: Logics in Artificial IntelligenceKI: German Conference on Artificial IntelligenceKRDB: Knowledge Representation Meets DatabasesMAAMAW: Modelling Autonomous Agents in a Multi-Agent WorldNC: ICSC Symposium on Neural ComputationPKDD: Principles of Data Mining and Knowledge DiscoverySBIA: Brazilian Symposium on Artificial IntelligenceScale-Space: Scale-Space Theories in Computer VisionXPS: Knowledge-Based SystemsI2CS: Innovative Internet Computing SystemsTARK: Theoretical Aspects of Rationality and Knowledge MeetingMKM: International Workshop on Mathematical Knowledge ManagementACIVS: International Conference on Advanced Concepts For Intelligent Vision Systems ATAL: Agent Theories, Architectures, and LanguagesLACL: International Conference on Logical Aspects of Computational LinguisticsAREA: Hardware and ArchitectureRank 1:ASPLOS: Architectural Support for Prog Lang and OSISCA: ACM/IEEE Symp on Computer ArchitectureICCAD: Intl Conf on Computer-Aided DesignDAC: Design Automation ConfMICRO: Intl Symp on MicroarchitectureHPCA: IEEE Symp on High-Perf Comp ArchitectureRank 2:FCCM: IEEE Symposium on Field Programmable Custom Computing MachinesSUPER: ACM/IEEE Supercomputing ConferenceICS: Intl Conf on SupercomputingISSCC: IEEE Intl Solid-State Circuits ConfHCS: Hot Chips SympVLSI: IEEE Symp VLSI CircuitsCODES+ISSS: Intl Conf on Hardware/Software Codesign & System SynthesisDATE: IEEE/ACM Design, Automation & Test in Europe ConferenceFPL: Field-Programmable Logic and ApplicationsCASES: International Conference on Compilers, Architecture, and Synthesis for Embedded Syste msRank 3:ICA3PP: Algs and Archs for Parall ProcEuroMICRO: New Frontiers of Information TechnologyACS: Australian Supercomputing ConfISC: Information Security ConferenceUnranked:Advanced Research in VLSIInternational Symposium on System SynthesisInternational Symposium on Computer DesignInternational Symposium on Circuits and SystemsAsia Pacific Design Automation ConferenceInternational Symposium on Physical DesignInternational Conference on VLSI DesignCANPC: Communication, Architecture, and Applications for Network-Based Parallel Computing CHARME: Conference on Correct Hardware Design and Verification MethodsCHES: Cryptographic Hardware and Embedded SystemsNDSS: Network and Distributed System Security SymposiumNOSA: Nordic Symposium on Software ArchitectureACAC: Australasian Computer Architecture ConferenceCSCC: WSES/IEEE world multiconference on Circuits, Systems, Communications & Computers ICN: IEEE International Conference on Networking Topology in Computer Science ConferenceAREA: Applications and MediaRank 1:I3DG: ACM-SIGRAPH Interactive 3D GraphicsSIGGRAPH: ACM SIGGRAPH ConferenceACM-MM: ACM Multimedia ConferenceDCC: Data Compression ConfSIGMETRICS: ACM Conf on Meas. & Modelling of Comp SysSIGIR: ACM SIGIR Conf on Information RetrievalPECCS: IFIP Intl Conf on Perf Eval of Comp \& Comm Sys WWW: World-Wide Web ConferenceRank 2:IEEE VisualizationEUROGRAPH: European Graphics ConferenceCGI: Computer Graphics InternationalCANIM: Computer AnimationPG: Pacific GraphicsICME: Intl Conf on MMedia & ExpoNOSSDAV: Network and OS Support for Digital A/VPADS: ACM/IEEE/SCS Workshop on Parallel \& Dist Simulation WSC: Winter Simulation ConferenceASS: IEEE Annual Simulation SymposiumMASCOTS: Symp Model Analysis \& Sim of Comp \& Telecom Sys PT: Perf Tools - Intl Conf on Model Tech \& Tools for CPE NetStore: Network Storage SymposiumMMCN: ACM/SPIE Multimedia Computing and NetworkingJCDL: Joint Conference on Digital LibrariesRank 3:ACM-HPC: ACM Hypertext ConfMMM: Multimedia ModellingDSS: Distributed Simulation SymposiumSCSC: Summer Computer Simulation ConferenceWCSS: World Congress on Systems SimulationESS: European Simulation SymposiumESM: European Simulation MulticonferenceHPCN: High-Performance Computing and NetworkingGeometry Modeling and ProcessingWISEDS-RT: Distributed Simulation and Real-time Applications IEEE Intl Wshop on Dist Int Simul and Real-Time Applications ECIR: European Colloquium on Information RetrievalEd-MediaIMSA: Intl Conf on Internet and MMedia SysUn-ranked:DVAT: IS\&T/SPIE Conf on Dig Video Compression Alg \& TechMME: IEEE Intl Conf. on Multimedia in EducationICMSO: Intl Conf on Modelling, Simulation and OptimisationICMS: IASTED Intl Conf on Modelling and SimulationCOTIM: Conference on Telecommunications and Information MarketsDOA: International Symposium on Distributed Objects and ApplicationsECMAST: European Conference on Multimedia Applications, Services and TechniquesGIS: Workshop on Advances in Geographic Information SystemsIDA: Intelligent Data AnalysisIDMS: Interactive Distributed Multimedia Systems and Telecommunication ServicesIUI: Intelligent User InterfacesMIS: Workshop on Multimedia Information SystemsWECWIS: Workshop on Advanced Issues of E-Commerce and Web/based Information Systems WIDM: Web Information and Data ManagementWOWMOM: Workshop on Wireless Mobile MultimediaWSCG: International Conference in Central Europe on Computer Graphics and Visualization LDTA: Workshop on Language Descriptions, Tools and ApplicationsIPDPSWPIM: International Workshop on Parallel and Distributed Computing Issues in Wireless N etworks and Mobile ComputingIWST: International Workshop on Scheduling and TelecommunicationsAPDCM: Workshop on Advances in Parallel and Distributed Computational ModelsCIMA: International ICSC Congress on Computational Intelligence: Methods and Applications FLA: Fuzzy Logic and Applications MeetingICACSD: International Conference on Application of Concurrency to System DesignICATPN: International conference on application and theory of Petri netsAICCSA: ACS International Conference on Computer Systems and ApplicationsCAGD: International Symposium of Computer Aided Geometric DesignSpanish Symposium on Pattern Recognition and Image AnalysisInternational Workshop on Cluster Infrastructure for Web Server and E-Commerce Applications WSES ISA: Information Science And Applications ConferenceCHT: International Symposium on Advances in Computational Heat TransferIMACS: International Conference on Applications of Computer AlgebraVIPromCom: International Symposium on Video Processing and Multimedia Communications PDMPR: International Workshop on Parallel and Distributed Multimedia Processing & Retrieval International Symposium On Computational And Applied PdesPDCAT: International Conference on Parallel and Distributed Computing, Applications, and Tec hniquesBiennial Computational Techniques and Applications ConferenceSymposium on Advanced Computing in Financial MarketsWCCE: World Conference on Computers in EducationITCOM: SPIE's International Symposium on The Convergence of Information Technologies and Com municationsConference on Commercial Applications for High-Performance ComputingMSA: Metacomputing Systems and Applications WorkshopWPMC : International Symposium on Wireless Personal Multimedia Communications WSC: Online World Conference on Soft Computing in Industrial Applications HERCMA: Hellenic European Research on Computer Mathematics and its Applications PARA: Workshop on Applied Parallel ComputingInternational Computer Science Conference: Active Media TechnologyIW-MMDBMS - Int. Workshop on Multi-Media Data Base Management SystemsAREA: System TechnologyRank 1:SIGCOMM: ACM Conf on Comm Architectures, Protocols & AppsINFOCOM: Annual Joint Conf IEEE Comp & Comm SocSPAA: Symp on Parallel Algms and ArchitecturePODC: ACM Symp on Principles of Distributed ComputingPPoPP: Principles and Practice of Parallel ProgrammingRTSS: Real Time Systems SympSOSP: ACM SIGOPS Symp on OS PrinciplesSOSDI: Usenix Symp on OS Design and ImplementationCCS: ACM Conf on Comp and Communications SecurityIEEE Symposium on Security and PrivacyMOBICOM: ACM Intl Conf on Mobile Computing and NetworkingUSENIX Conf on Internet Tech and SysICNP: Intl Conf on Network ProtocolsPACT: Intl Conf on Parallel Arch and Compil TechRTAS: IEEE Real-Time and Embedded Technology and Applications Symposium ICDCS: IEEE Intl Conf on Distributed Comp SystemsRank 2:CC: Compiler ConstructionIPDPS: Intl Parallel and Dist Processing SympIC3N: Intl Conf on Comp Comm and NetworksICPP: Intl Conf on Parallel ProcessingSRDS: Symp on Reliable Distributed SystemsMPPOI: Massively Par Proc Using Opt InterconnsASAP: Intl Conf on Apps for Specific Array ProcessorsEuro-Par: European Conf. on Parallel ComputingFast Software EncryptionUsenix Security SymposiumEuropean Symposium on Research in Computer SecurityWCW: Web Caching WorkshopLCN: IEEE Annual Conference on Local Computer NetworksIPCCC: IEEE Intl Phoenix Conf on Comp & CommunicationsCCC: Cluster Computing ConferenceICC: Intl Conf on CommWCNC: IEEE Wireless Communications and Networking ConferenceCSFW: IEEE Computer Security Foundations WorkshopRank 3:MPCS: Intl. Conf. on Massively Parallel Computing SystemsGLOBECOM: Global CommICCC: Intl Conf on Comp CommunicationNOMS: IEEE Network Operations and Management SympCONPAR: Intl Conf on Vector and Parallel ProcessingVAPP: Vector and Parallel ProcessingICPADS: Intl Conf. on Parallel and Distributed SystemsPublic Key CryptosystemsAnnual Workshop on Selected Areas in CryptographyAustralasia Conference on Information Security and PrivacyInt. Conf on Inofrm and Comm. SecurityFinancial CryptographyWorkshop on Information HidingSmart Card Research and Advanced Application ConferenceICON: Intl Conf on NetworksNCC: Nat Conf CommIN: IEEE Intell Network WorkshopSoftcomm: Conf on Software in Tcomms and Comp NetworksINET: Internet Society ConfWorkshop on Security and Privacy in E-commerceUn-ranked:PARCO: Parallel ComputingSE: Intl Conf on Systems Engineering (**)PDSECA: workshop on Parallel and Distributed Scientific and Engineering Computing with Appli cationsCACS: Computer Audit, Control and Security ConferenceSREIS: Symposium on Requirements Engineering for Information SecuritySAFECOMP: International Conference on Computer Safety, Reliability and SecurityIREJVM: Workshop on Intermediate Representation Engineering for the Java Virtual Machine EC: ACM Conference on Electronic CommerceEWSPT: European Workshop on Software Process TechnologyHotOS: Workshop on Hot Topics in Operating SystemsHPTS: High Performance Transaction SystemsHybrid SystemsICEIS: International Conference on Enterprise Information SystemsIOPADS: I/O in Parallel and Distributed SystemsIRREGULAR: Workshop on Parallel Algorithms for Irregularly Structured ProblemsKiVS: Kommunikation in Verteilten SystemenLCR: Languages, Compilers, and Run-Time Systems for Scalable ComputersMCS: Multiple Classifier SystemsMSS: Symposium on Mass Storage SystemsNGITS: Next Generation Information Technologies and SystemsOOIS: Object Oriented Information SystemsSCM: System Configuration ManagementSecurity Protocols WorkshopSIGOPS European WorkshopSPDP: Symposium on Parallel and Distributed ProcessingTreDS: Trends in Distributed SystemsUSENIX Technical ConferenceVISUAL: Visual Information and Information SystemsFoDS: Foundations of Distributed Systems: Design and Verification of Protocols conference RV: Post-CAV Workshop on Runtime VerificationICAIS: International ICSC-NAISO Congress on Autonomous Intelligent SystemsITiCSE: Conference on Integrating Technology into Computer Science EducationCSCS: CyberSystems and Computer Science ConferenceAUIC: Australasian User Interface ConferenceITI: Meeting of Researchers in Computer Science, Information Systems Research & Statistics European Conference on Parallel ProcessingRODLICS: Wses International Conference on Robotics, Distance Learning & Intelligent Communic ation SystemsInternational Conference On Multimedia, Internet & Video TechnologiesPaCT: Parallel Computing Technologies workshopPPAM: International Conference on Parallel Processing and Applied MathematicsInternational Conference On Information Networks, Systems And TechnologiesAmiRE: Conference on Autonomous Minirobots for Research and EdutainmentDSN: The International Conference on Dependable Systems and NetworksIHW: Information Hiding WorkshopGTVMT: International Workshop on Graph Transformation and Visual Modeling Techniques AREA: Programming Languages and Software EngineeringRank 1:POPL: ACM-SIGACT Symp on Principles of Prog LangsPLDI: ACM-SIGPLAN Symp on Prog Lang Design & ImplOOPSLA: OO Prog Systems, Langs and ApplicationsICFP: Intl Conf on Function ProgrammingJICSLP/ICLP/ILPS: (Joint) Intl Conf/Symp on Logic ProgICSE: Intl Conf on Software EngineeringFSE: ACM Conf on the Foundations of Software Engineering (inc: ESEC-FSE) FM/FME: Formal Methods, World Congress/EuropeCAV: Computer Aided VerificationRank 2:CP: Intl Conf on Principles & Practice of Constraint ProgTACAS: Tools and Algos for the Const and An of SystemsESOP: European Conf on ProgrammingICCL: IEEE Intl Conf on Computer LanguagesPEPM: Symp on Partial Evalutation and Prog ManipulationSAS: Static Analysis SymposiumRTA: Rewriting Techniques and ApplicationsIWSSD: Intl Workshop on S/W Spec & DesignCAiSE: Intl Conf on Advanced Info System EngineeringSSR: ACM SIGSOFT Working Conf on Software ReusabilitySEKE: Intl Conf on S/E and Knowledge EngineeringICSR: IEEE Intl Conf on Software ReuseASE: Automated Software Engineering ConferencePADL: Practical Aspects of Declarative LanguagesISRE: Requirements EngineeringICECCS: IEEE Intl Conf on Eng. of Complex Computer SystemsIEEE Intl Conf on Formal Engineering MethodsIntl Conf on Integrated Formal MethodsFOSSACS: Foundations of Software Science and Comp StructAPLAS: Asian Symposium on Programming Languages and SystemsMPC: Mathematics of Program ConstructionECOOP: European Conference on Object-Oriented ProgrammingICSM: Intl. Conf on Software MaintenanceHASKELL - Haskell WorkshopRank 3:FASE: Fund Appr to Soft EngAPSEC: Asia-Pacific S/E ConfPAP/PACT: Practical Aspects of PROLOG/Constraint TechALP: Intl Conf on Algebraic and Logic ProgrammingPLILP: Prog, Lang Implentation & Logic ProgrammingLOPSTR: Intl Workshop on Logic Prog Synthesis & TransfICCC: Intl Conf on Compiler ConstructionCOMPSAC: Intl. Computer S/W and Applications ConfTAPSOFT: Intl Joint Conf on Theory & Pract of S/W DevWCRE: SIGSOFT Working Conf on Reverse EngineeringAQSDT: Symp on Assessment of Quality S/W Dev ToolsIFIP Intl Conf on Open Distributed ProcessingIntl Conf of Z UsersIFIP Joint Int'l Conference on Formal Description Techniques and Protocol Specification, Tes ting, And VerificationPSI (Ershov conference)UML: International Conference on the Unified Modeling LanguageUn-ranked:Australian Software Engineering ConferenceIEEE Int. W'shop on Object-oriented Real-time Dependable Sys. (WORDS)IEEE International Symposium on High Assurance Systems EngineeringThe Northern Formal Methods WorkshopsFormal Methods PacificInt. Workshop on Formal Methods for Industrial Critical SystemsJFPLC - International French Speaking Conference on Logic and Constraint ProgrammingL&L - Workshop on Logic and LearningSFP - Scottish Functional Programming WorkshopLCCS - International Workshop on Logic and Complexity in Computer ScienceVLFM - Visual Languages and Formal MethodsNASA LaRC Formal Methods WorkshopPASTE: Workshop on Program Analysis For Software Tools and EngineeringTLCA: Typed Lambda Calculus and ApplicationsFATES - A Satellite workshop on Formal Approaches to Testing of SoftwareWorkshop On Java For High-Performance ComputingDSLSE - Domain-Specific Languages for Software EngineeringFTJP - Workshop on Formal Techniques for Java ProgramsWFLP - International Workshop on Functional and (Constraint) Logic ProgrammingFOOL - International Workshop on Foundations of Object-Oriented LanguagesSREIS - Symposium on Requirements Engineering for Information SecurityHLPP - International workshop on High-level parallel programming and applicationsINAP - International Conference on Applications of PrologMPOOL - Workshop on Multiparadigm Programming with OO LanguagesPADO - Symposium on Programs as Data ObjectsTOOLS: Int'l Conf Technology of Object-Oriented Languages and SystemsAustralasian Conference on Parallel And Real-Time SystemsPASTE: Workshop on Program Analysis For Software Tools and EngineeringAvoCS: Workshop on Automated Verification of Critical SystemsSPIN: Workshop on Model Checking of SoftwareFemSys: Workshop on Formal Design of Safety Critical Embedded SystemsAda-EuropePPDP: Principles and Practice of Declarative ProgrammingAPL ConferenceASM: Workshops on Abstract State MachinesCOORDINATION: Coordination Models and LanguagesDocEng: ACM Symposium on Document EngineeringDSV-IS: Design, Specification, and Verification of Interactive SystemsFMCAD: Formal Methods in Computer-Aided DesignFMLDO: Workshop on Foundations of Models and Languages for Data and ObjectsIFL: Implementation of Functional LanguagesILP: International Workshop on Inductive Logic ProgrammingISSTA: International Symposium on Software Testing and AnalysisITC: International Test ConferenceIWFM: Irish Workshop in Formal MethodsJava GrandeLP: Logic Programming: Japanese ConferenceLPAR: Logic Programming and Automated ReasoningLPE: Workshop on Logic Programming EnvironmentsLPNMR: Logic Programming and Non-monotonic ReasoningPJW: Workshop on Persistence and JavaRCLP: Russian Conference on Logic ProgrammingSTEP: Software Technology and Engineering PracticeTestCom: IFIP International Conference on Testing of Communicating SystemsVL: Visual LanguagesFMPPTA: Workshop on Formal Methods for Parallel Programming Theory and Applications WRS: International Workshop on Reduction Strategies in Rewriting and Programming FATES: A Satellite workshop on Formal Approaches to Testing of Software FORMALWARE: Meeting on Formalware Engineering: Formal Methods for Engineering Software DRE: conference Data Reverse EngineeringSTAREAST: Software Testing Analysis & Review ConferenceConference on Applied Mathematics and Scientific ComputingInternational Testing Computer Software ConferenceLinux Showcase & ConferenceFLOPS: International Symposum on Functional and Logic ProgrammingGCSE: International Conference on Generative and Component-Based Software Engineering JOSES: Java Optimization Strategies for Embedded Systems。

人工智能：模型与算法_浙江大学中国大学mooc课后章节答案期末考试题库2023年

人工智能：模型与算法_浙江大学中国大学mooc课后章节答案期末考试题库2023年1.下面哪个描述不属于邱奇－图灵论题所包含的意思（）参考答案:任何表达力足够强的(递归可枚举)形式系统同时满足一致性和完备性2.在本课程内容范围内，“在状态s，按照某个策略采取动作a后在未来所获得反馈值的期望”，这句话描述了状态s的（ )参考答案:动作-价值函数3.德国著名数学家希尔伯特在1900年举办的国际数学家大会中所提出的“算术公理的相容性(the compatibility of the arithmetical axioms)”这一问题推动了可计算思想研究的深入。

在希尔伯特所提出的这个问题中，一个算术公理系统是相容的需要满足三个特点。

下面哪个描述不属于这三个特点之一（）参考答案:复杂性，即算法性能与输入数据大小相关4.下面哪句话描述了现有深度学习这一种人工智能方法的特点（）参考答案:大数据，小任务5.下面哪个说法是不正确的（）参考答案:一个有向无环图无法唯一地决定一个联合分布6.下面哪句话语较为恰当刻画了监督学习方法中生成方法的特点（）参考答案:授之于鱼、不如授之于“渔”7.假设原始数据个数为n，原始数据维数为d，降维后的维数为l，下面对主成分分析算法描述不正确的是（）参考答案:主成分分析学习得到了l个d维大小的向量，这l个d维向量之间彼此相关8.逻辑斯蒂回归和线性区别分析均可完成分类任务，下面描述正确的是（）参考答案:逻辑斯蒂回归可直接在数据原始空间进行分类，线性区别分析需要在降维所得空间中进行分类9.下面对逻辑斯蒂回归（logistic regression)和多项逻辑斯蒂回归模型(multi-nominal logistic model)描述不正确的是（）参考答案:逻辑斯帝回归是监督学习，多项逻辑斯蒂回归模型是非监督学习10.在神经网络学习中，每个神经元会完成若干功能，下面哪个功能不是神经元所能够完成的功能（）参考答案:向前序相邻神经元反馈加权累加信息11.下面对前馈神经网络描述不正确的是（）参考答案:同一层内神经元之间存在全连接12.下面对感知机网络（Perceptron Networks）描述不正确的是（）参考答案:感知机网络具有一层隐藏层13.下面对梯度下降方法描述不正确的是（）参考答案:梯度方向是函数值下降最快方向14.我们可以将深度学习看成一种端到端的学习方法，这里的端到端指的是（）参考答案:输入端-输出端15.在前馈神经网络中，误差后向传播（BP算法）将误差从输出端向输入端进行传输的过程中，算法会调整前馈神经网络的什么参数（）参考答案:相邻层神经元和神经元之间的连接权重16.前馈神经网络通过误差后向传播（BP算法）进行参数学习，这是一种（）机器学习手段参考答案:监督学习17.下面对前馈神经网络这种深度学习方法描述不正确的是（）参考答案:隐藏层数目大小对学习性能影响不大18.下面对浅层学习和深度学习描述不正确的是（）参考答案:浅层学习仅能实现线性映射、深度学习可以实现非线性映射19.卷积操作是卷积神经网络所具备的一个重要功能，对一幅图像进行高斯卷积操作的作用是（）参考答案:对图像进行平滑（模糊化）20.对完成特定任务的卷积神经网络训练采用的是监督学习方法。

机器人一级考试详细知识点

一鸣机器人教育——机器人一级考试知识点机器人一级考试知识点一鸣机器人教育第一部分：机器人的相关知识：1.机器人的英文：Robot2.机器人三大定律：a)第一条：机器人不应伤害人类。

第二条：机器人必须服从人类的命令，与第一条违背的命令除外。

b)第三条：机器人应能保护自己，与前两条抵触者除外。

c)工业机器人之父。

美国约瑟夫·英格伯格和德沃尔创造出第一台工业机器人，被称为 3.4.主流机器人的影像以及其中的机器人：《剪刀手爱德华》、《超能陆战队》、《变形金刚》、《机器人总动员》等5.机器人系统基本结构（只有三条）：机械部分、传感部分、控制部分。

2008 年 6 月第12 届机器人世界杯在中国举办。

6.恐怖谷理论：随着机器人的拟人程度增加，人类对它的好感度就会改变（反感）7.第二部分：书本知识基本结构一：钉子、螺丝钉、螺丝杆、螺母、楔形（斜面）、、螺丝刀、扳手的辨别运用了三角形的稳定性二、秋千：单摆原理（理解）高度：最低点→最高点→最低点速度：速度最大→速度为零→速度最大（机能量：动能最大→势能最大→动能最大械能永远不可能为）0单摆：单摆运动的周期T 和摆幅以及物体的重量无关，与摆长和重力加速度g 有关。

1一鸣机器人教育——机器人一级考试知识点物体稳定性分析：1)与地面接触面积越大，物体越稳。

重心越低，物体越稳。

2)3)通过重心作竖直向下的直线与地面的交点，如果在接触面上，则物体较稳，若在接触面外，物体不稳。

能量守恒：能量不会凭空消失，也不会凭空产生，它只会从一种形式转化为其他形式，或者从一个物体转移到另一个物体，而在转化和转移过程中，能量的总和保持不变。

三、跷跷板杠杆原理（杠杆：能绕某支点转动的杆）--阿基米给我一个支点，我就能撬起整个地球德）L1＝F2×L2F1（×另一边物体到支点的距X X 物体到支点的距离=另一边的重量一边的重量离。

支点到力的作用线的距离叫力臂（易错点）！：杠杆分类（常见杠杆分类）应用快速辨别：一般来说杠杆上徒手办的到的但是用杠杆办的更轻松就是省力杠杆四、搅拌器（打蛋器）齿轮和轮轴齿轮：是一种轮缘上有齿且能连续啮合传递运动和动力的机械零件。

图像处理领域公认的重要英文期刊和会议分级

人工智能和图像处理方面的各种会议的评级2010年8月31日忙菇发表评论阅读评论人工智能和图像处理方面的各种会议的评级澳大利亚政府和澳大利亚研究理事会做的，有一定参考价值会议名称会议缩写评级ACM SIG International Conference on Computer Graphics and Interactive Techniques SIGGRAPH AACM Virtual Reality Software and Technology VRST AACM/SPIE Multimedia Computing and Networking MMCN AACM-SIGRAPH Interactive 3D Graphics I3DG AAdvances in Neural Information Processing Systems NIPS AAnnual Conference of the Cognitive Science Society CogSci AAnnual Conference of the International Speech Communication Association (was Eurospeech) Interspeech AAnnual Conference on Computational Learning Theory COLT AArtificial Intelligence in Medicine AIIM AArtificial Intelligence in Medicine in Europe AIME AAssociation of Computational Linguistics ACL ACognitive Science Society Annual Conference CSSAC AComputer Animation CANIM AConference in Uncertainty in Artificial Intelligence UAI AConference on Natural Language Learning CoNLL AEmpirical Methods in Natural Language Processing EMNLP AEuropean Association of Computational Linguistics EACL AEuropean Conference on Artificial Intelligence ECAI AEuropean Conference on Computer Vision ECCV AEuropean Conference on Machine Learning ECML AEuropean Conference on Speech Communication and Technology (now Interspeech) EuroSpeech AEuropean Graphics Conference EUROGRAPH AFoundations of Genetic Algorithms FOGA AIEEE Conference on Computer Vision and Pattern Recognition CVPR AIEEE Congress on Evolutionary Computation IEEE CEC AIEEE Information Visualization Conference IEEE InfoVis AIEEE International Conference on Computer Vision ICCV AIEEE International Conference on Fuzzy Systems FUZZ-IEEE AIEEE International Joint Conference on Neural Networks IJCNN AIEEE International Symposium on Artificial Life IEEE Alife AIEEE Visualization IEEE VIS AIEEE Workshop on Applications of Computer Vision WACV AIEEE/ACM International Conference on Computer-Aided Design ICCAD AIEEE/ACM International Symposium on Mixed and Augmented Reality ISMAR A International Conference on Automated Deduction CADE AInternational Conference on Autonomous Agents and Multiagent Systems AAMAS A International Conference on Computational Linguistics COLING AInternational Conference on Computer Graphics Theory and Application GRAPP A International Conference on Intelligent Tutoring Systems ITS AInternational Conference on Machine Learning ICML AInternational Conference on Neural Information Processing ICONIP AInternational Conference on the Principles of Knowledge Representation and Reasoning KR A International Conference on the Simulation and Synthesis of Living Systems ALIFE A International Joint Conference on Artificial Intelligence IJCAI AInternational Joint Conference on Automated Reasoning IJCAR AInternational Joint Conference on Qualitative and Quantitative Practical Reasoning ESQARU A Medical Image Computing and Computer-Assisted Intervention MICCAI ANational Conference of the American Association for Artificial Intelligence AAAI ANorth American Association for Computational Linguistics NAACL APacific Conference on Computer Graphics and Applications PG AParallel Problem Solving from Nature PPSN AACM SIGGRAPH/Eurographics Symposium on Computer Animation SCA BAdvanced Concepts for Intelligent Vision Systems ACIVS BAdvanced Visual Interfaces AVI BAgent-Oriented Information Systems Workshop AOIS BAnnual International Workshop on Presence PRESENCE BArtificial Neural Networks in Engineering Conference ANNIE BAsian Conference on Computer Vision ACCV BAsia-Pacific Conference on Simulated Evolution and Learning SEAL BAustralasian Conference on Robotics and Automation ACRA BAustralasian Joint Conference on Artificial Intelligence AI BAustralasian Speech Science and Technology S ST BAustralian Conference for Knowledge Management and Intelligent Decision Support A CKMIDS B Australian Conference on Artificial Life ACAL BAustralian Symposium on Information Visualisation ASIV BBritish Machine Vision Conference B MVC BCanadian Artificial Intelligence Conference CAAI BComputer Graphics International CGI BConference of the Association for Machine Translation in the Americas AMTA B Conference of the European Association for Machine Translation EAMT BConference of the Pacific Association for Computational Linguistics PACLING BConference on Artificial Intelligence for Applications CAIA BCongress of the Italian Assoc for AI AI*IA BDeutsche Arbeitsgemeinschaft für Mustererkennung DAGM e.V DAGM BDigital Image Computing Techniques and Applications DICTA BEurographics Symposium on Parallel Graphics and Visualization EGPGV BEurographics/IEEE Symposium on Visualization EuroVis BEuropean Conference on Artificial Life ECAL BEuropean Conference on Genetic Programming EUROGP BEuropean Simulation Symposium ESS BEuropean Symposium on Artificial Neural Networks ESANN BFrench Conference on Knowledge Acquisition and Machine Learning FCKAML BGerman Conference on Multi-Agent system Technologies MATES BGraphics Interface GI BIEEE International Conference on Image Processing ICIP BIEEE International Conference on Multimedia and Expo ICME BIEEE International Conference on Neural Networks ICNN BIEEE International Workshop on Visualizing Software for Understanding and Analysis VISSOFT BIEEE Pacific Visualization Symposium (was APVIS) PacificVis BIEEE Symposium on 3D User Interfaces 3DUI BIEEE Virtual Reality Conference VR BIFSA World Congress IFSA BImage and Vision Computing Conference IVCNZ BInnovative Applications in AI IAAI BIntegration of Software Engineering and Agent Technology ISEAT BIntelligent Virtual Agents IVA BInternational Cognitive Robotics Conference COGROBO BInternational Conference on Advances in Intelligent Systems: Theory and Applications AISTABInternational Conference on Artificial Intelligence and Statistics AISTATS BInternational Conference on Artificial Neural Networks ICANN BInternational Conference on Artificial Reality and Telexistence ICAT BInternational Conference on Computer Analysis of Images and Patterns CAIP BInternational Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast Asia S IGGRAPH ASIA BInternational Conference on Database and Expert Systems Applications DEXA B International Conference on Frontiers of Handwriting Recognition ICFHR BInternational Conference on Genetic Algorithms ICGA BInternational Conference on Image Analysis and Processing ICIAP BInternational Conference on Implementation and Application of Automata CIAA B International Conference on Information Visualisation IV BInternational Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming for Combinatorial Optimization Problems CPAIOR B International Conference on Intelligent Systems and Knowledge Engineering ISKE B International Conference on Intelligent Text Processing and Computational Linguistics CICLING BInternational Conference on Knowledge Science, Engineering and Management KSEM B International Conference on Modelling Decisions for Artificial Intelligence MDAI B International Conference on Multiagent Systems ICMS BInternational Conference on Pattern Recognition ICPR BInternational Conference on Software Engineering and Knowledge Engineering SEKE B International Conference on Theoretical and Methodological Issues in machine Translation TMI BInternational Conference on Tools with Artificial Intelligence ICTAI BInternational Conference on Ubiquitous and Intelligence Computing UIC BInternational Conference on User Modelling (now UMAP) UM BInternational Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision WSCG BInternational Fuzzy Logic and Intelligent technologies in Nuclear Science Conference F LINS B International Joint Conference on Natural Language Processing IJCNLP BInternational Meeting on DNA Computing and Molecular Programming DNA BInternational Natural Language Generation Conference INLG BInternational Symposium on Artificial Intelligence and Maths ISAIM BInternational Symposium on Computational Life Science CompLife BInternational Symposium on Mathematical Morphology ISMM BInternational Work-Conference on Artificial and Natural Neural Networks IWANN B International Workshop on Agents and Data Mining Interaction ADMI BInternational Workshop on Ant Colony ANTS BInternational Workshop on Paraphrasing IWP BInternational Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises WETICE BJoint workshop on Multimodal Interaction and Related Machine Learning Algorithms (nowICMI-MLMI) MLMI BLogic and Engineering of Natural Language Semantics LENLS BMachine Translation Summit MT SUMMIT BPacific Asia Conference on Language, Information and Computation PACLIC BPacific Asian Conference on Expert Systems PACES BPacific Rim International Conference on Artificial Intelligence PRICAI BPacific Rim International Workshop on Multi-Agents PRIMA BPacific-Rim Symposium on Image and Video Technology PSIVT BPortuguese Conference on Artificial Intelligence EPIA BRobot Soccer World Cup RoboCup BScandinavian Conference on Artificial Intelligence S CAI BSingapore International Conference on Intelligent Systems SPICIS BSPIE International Conference on Visual Communications and Image Processing VCIP B Summer Computer Simulation Conference SCSC BSymposium on Logical Formalizations of Commonsense Reasoning COMMONSENSE B The Theory and Application of Diagrams DIAGRAMS BWinter Simulation Conference WSC BWorld Congress on Expert Systems WCES BWorld Congress on Neural Networks WCNN B3-D Digital Imaging and Modelling 3DIM CACM Workshop on Secure Web Services SWS CAdvanced Course on Artificial Intelligence ACAI CAdvances in Intelligent Systems AIS CAgent-Oriented Software Engineering Workshop AOSE CAmbient Intelligence Developments Aml.d CAnnual Conference on Evolutionary Programming EP CApplications of Information Visualization IV-App CApplied Perception in Graphics and Visualization APGV CArgentine Symposium on Artificial Intelligence ASAI CArtificial Intelligence in Knowledge Management AIKM CAsia-Pacific Conference on Complex Systems C omplex CAsia-Pacific Symposium on Visualisation APVIS CAustralasian Cognitive Science Society Conference AuCSS CAustralia-Japan Joint Workshop on Intelligent and Evolutionary Systems AJWIES C Australian Conference on Neural Networks ACNN CAustralian Knowledge Acquisition Workshop AKAW CAustralian MADYMO Users Meeting MADYMO CBioinformatics Visualization BioViz CBrazilian Symposium on Computer Graphics and Image Processing SIBGRAPI C Canadian Conference on Computer and Robot Vision CRV CComplex Objects Visualization Workshop COV CComputer Animation, Information Visualisation, and Digital Effects CAivDE C Conference of the International Society for Decision Support Systems I SDSS C Conference on Artificial Neural Networks and Expert systems ANNES CConference on Visualization and Data Analysis VDA CCooperative Design, Visualization, and Engineering CDVE CCoordinated and Multiple Views in Exploratory Visualization CMV CCultural Heritage Knowledge Visualisation CHKV CDesign and Aesthetics in Visualisation DAViz CDiscourse Anaphora and Anaphor Resolution Colloquium DAARC CENVI and IDL Data Analysis and Visualization Symposium VISualize CEuro Virtual Reality Euro VR CEuropean Conference on Ambient Intelligence AmI CEuropean Conference on Computational Learning Theory (Now in COLT) EuroCOLT C European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty ECSQARU CEuropean Congress on Intelligent Techniques and Soft Computing EUFIT CEuropean Workshop on Modelling Autonomous Agents in a Multi-Agent World MAAMAW C European Workshop on Multi-Agent Systems EUMAS CFinite Differences-Finite Elements-Finite Volumes-Boundary Elements F-and-B CFlexible Query-Answering Systems FQAS CFlorida Artificial Intelligence Research Society Conference FlAIRS CFrench Speaking Conference on the Extraction and Management of Knowledge EGC C GeoVisualization and Information Visualization GeoViz CGerman Conference on Artificial Intelligence K I CHellenic Conference on Artificial Intelligence S ETN CHungarian National Conference on Agent Based Computation HUNABC CIberian Conference on Pattern Recognition and Image Analysis IBPRIA CIberoAmerican Congress on Pattern Recognition CIARP CIEEE Automatic Speech Recognition and Understanding Workshop ASRU CIEEE International Conference on Adaptive and Intelligent Systems ICAIS CIEEE International Conference on Automatic Face and Gesture Recognition FG CIEEE International Conference on Cognitive Informatics ICCI CIEEE International Conference on Computational Cybernetics ICCC CIEEE International Conference on Computational Intelligence for Measurement Systems and Applications CIMSA CIEEE International Conference on Cybernetics and Intelligent Systems CIS CIEEE International Conference on Granular Computing GrC CIEEE International Conference on Information and Automation IEEE ICIA CIEEE International Conference on Intelligence for Homeland Security and Personal Safety CIHSPS CIEEE International Conference on Intelligent Computer Communication and Processing ICCP C IEEE International Conference on Intelligent Systems IEEE IS CIEEE International Geoscience and Remote Sensing Symposium IGARSS CIEEE International Symposium on Multimedia ISM CIEEE International Workshop on Cellular Nanoscale Networks and Applications CNNA CIEEE International Workshop on Neural Networks for Signal Processing NNSP CIEEE Swarm Intelligence Symposium IEEE SIS CIEEE Symposium on Computational Intelligence and Data Mining IEEE CIDM CIEEE Symposium on Computational Intelligence and Games CIG CIEEE Symposium on Computational Intelligence for Financial Engineering IEEE CIFEr C IEEE Symposium on Computational intelligence for Image Processing IEEE CIIP CIEEE Symposium on Computational intelligence for Multimedia Signal and Vision Processing IEEE CIMSVP CIEEE Symposium on Computational Intelligence for Security and Defence Applications IEEE CISDA CIEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology IEEE CIBCB CIEEE Symposium on Computational Intelligence in Control and Automation IEEE CICA C IEEE Symposium on Computational Intelligence in Cyber Security IEEE CICS CIEEE Symposium on Computational Intelligence in Image and Signal Processing CIISP C IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making IEEE MCDM CIEEE Symposium on Computational Intelligence in Scheduling IEEE CI-Sched CIEEE Symposium on Intelligent Agents IEEE IA CIEEE Workshop on Computational Intelligence for Visual Intelligence IEEE CIVI CIEEE Workshop on Computational Intelligence in Aerospace Applications IEEE CIAA CIEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications IEEE CIB CIEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems IEEE CIWS CIEEE Workshop on Computational Intelligence in Virtual Environments IEEE CIVE CIEEE Workshop on Evolvable and Adaptive Hardware IEEE WEAH CIEEE Workshop on Evolving and Self-Developing Intelligent Systems IEEE ESDIS CIEEE Workshop on Hybrid Intelligent Models and Applications IEEE HIMA CIEEE Workshop on Memetic Algorithms IEEE WOMA CIEEE Workshop on Organic Computing IEEE OC CIEEE Workshop on Robotic Intelligence in Informationally Structured Space IEEE RiiSS C IEEE Workshop on Speech Coding SCW CIEEE/WIC/ACM International Conference on Intelligent Agent Technology IAT CIEEE/WIC/ACM international Conference on Web Intelligence and Intelligent Agent Technology WI-IAT CIFIP Conference on Biologically Inspired Collaborative Computing BICC CInformation Visualisation Theory and Practice InfVis CInformation Visualization Evaluation IVE CInformation Visualization in Biomedical Informatics IVBI CIntelligence Tools, Data Mining, Visualization IDV CIntelligent Multimedia, Video and Speech Processing Symposium MVSP C International Atlantic Web Intelligence Conference AWIC CInternational Colloquium on Data Sciences, Knowledge Discovery and Business Intelligence DSKDB CInternational Conference Computer Graphics, Imaging and Visualization CGIV CInternational Conference Formal Concept Analysis Conference ICFCA CInternational Conference Imaging Science, Systems and Technology CISST CInternational Conference on 3G Mobile Communication Technologies 3G CInternational Conference on Adaptive and Natural Computing Algorithms ICANNGA C International Conference on Advances in Pattern Recognition and Digital Techniques ICAPRDT CInternational Conference on Affective Computing and Intelligent A CII CInternational Conference on Agents and Artificial Intelligence ICAART CInternational Conference on Artificial Intelligence I C-AI CInternational Conference on Artificial Intelligence and Law ICAIL CInternational Conference on Artificial Intelligence and Pattern Recognition A IPR CInternational Conference on Artificial Intelligence and Soft Computing ICAISC C International Conference on Artificial Intelligence in Science and Technology AISAT C International Conference on Arts and Technology ArtsIT CInternational Conference on Case-Based Reasoning Research and Development ICCBR C International Conference on Computational Collective Intelligence: Semantic Web, Social Networks and Multiagent Systems ICCCI CInternational Conference on Computational Intelligence and Multimedia ICCIMA C International Conference on Computational Intelligence and Software Engineering CISE C International Conference on Computational Intelligence for Modelling, Control and Automation CIMCA CInternational Conference on Computational Intelligence, Robotics and Autonomous Systems CIRAS CInternational Conference on Computational Semiotics for Games and New Media Cosign C International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa AFRIGRAPH CInternational Conference on Computer Theory and Applications ICCTA CInternational Conference on Computer Vision Systems I CVS CInternational Conference on Cybercrime Forensics Education and Training CFET CInternational Conference on Engineering Applications of Neural Networks EANN C International Conference on Evolutionary Computation ICEC CInternational Conference on Fuzzy Systems and Knowledge FSKD CInternational Conference on Hybrid Artificial Intelligence Systems HAIS CInternational Conference on Hybrid Intelligent Systems HIS CInternational Conference on Image and Graphics ICIG CInternational Conference on Image and Signal Processing ICISP CInternational Conference on Immersive Telecommunications IMMERSCOM CInternational Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE CInternational Conference on Information and Knowledge Engineering I KE CInternational Conference on Intelligent Systems ICIL CInternational Conference on Intelligent Systems Designs and Applications ISDA CInternational Conference on Knowledge Engineering and Ontology KEOD CInternational Conference on Knowledge-based Intelligent Electronic Systems KIES CInternational Conference on Machine Learning and Applications ICMLA CInternational Conference on Machine Learning and Cybernetics ICMLC CInternational Conference on Machine Vision ICMV CInternational Conference on Medical Information Visualisation MediVis CInternational Conference on Modelling, Simulation and Optimisation ICMSO CInternational Conference on Natural Computation ICNC CInternational Conference on Neural, Parallel and Scientific Computations NPSC C International Conference on Principles of Practice in Multi-Agent Systems PRIMA C International Conference on Recent Advances in Natural Language Processing RANLP C International Conference on Rough Sets and Current Trends in Computing RSCTC C International Conference on Spoken Language Processing ICSLP CInternational Conference on the Foundations of Digital Games FDG CInternational Conference on Vision Theory and Applications VISAPP CInternational Conference on Visual Information Systems VISUAL CInternational Conference on Web-based Modelling and Simulation WebSim CInternational Congress on Modelling and Simulation MODSIM CInternational ICSC Congress on Intelligent Systems and Applications IICISA CInternational KES Symposium on Agents and Multiagent systems – Technologies and Applications KES AMSTA CInternational Machine Vision and Image Processing Conference IMVIP CInternational Symposium on 3D Data Processing Visualization and Transmission 3DPVT C International Symposium on Applied Computational Intelligence and Informatics SACI C International Symposium on Applied Machine Intelligence and Informatics SAMI C International Symposium on Artificial Life and Robotics AROB CInternational Symposium on Audio, Video, Image Processing and Intelligent Applications ISAVIIA CInternational Symposium on Foundations of Intelligent Systems ISMIS CInternational Symposium on Innovations in Intelligent Systems and Applications INISTA C International Symposium on Neural Networks ISNN CInternational Symposium on Visual Computing ISVC CInternational Visualization in Transportation Symposium and Workshop TRB Viz C International Workshop on Combinations of Intelligent Methods and Applications CIMA C International Workshop on Genetic and Evolutionary Fuzzy Systems GEFS CInternational Workshop on Human Aspects in Ambient Intelligence: Agent Technology, Human-Oriented Knowledge and Applications HAI CInternational Workshop on Image Analysis and Information Fusion IAIF CInternational Workshop on Intelligent Agents IWIA CInternational Workshop on Knowledge Discovery from Data Streams IWKDDS CInternational Workshop on MultiAgent Based Simulation MABS CInternational Workshop on Nonmonotonic Reasoning, Action and Change NRAC C International Workshop on Soft Computing Applications SOFA CInternational Workshop on Ubiquitous Virtual Reality IWUVR CINTUITION International Conference INTUITION CISCA Tutorial and Research Workshop Automatic Speech Recognition ASR CJoint Australia and New Zealand Biennial Conference on Digital Image and Vision Computing DIVC CJoint Conference on New Methods in Language Processing and Computational Natural Language Learning NeMLaP CKES International Symposium on Intelligent Decision Technologies KES IDT CKnowledge Domain Visualisation KDViz CKnowledge Visualization and Visual Thinking KV CMachine Vision Applications MVA CNAISO Congress on Autonomous Intelligent Systems NAISO CNatural Language Processing and Knowledge Engineering IEEE NLP-KE CNorth American Fuzzy Information Processing Society Conference NAFIPS CPacific-Rim Conference on Multimedia PCM CPan-Sydney Area Workshop on Visual Information Processing VIP CPractical Application of Intelligent Agents and Multi-Agent Technology Conference PAAM C Program Visualization Workshop PVW CSemantic Web Visualisation VSW CSGAI International Conference on Artificial Intelligence SGAI CSimulation Technology and Training Conference SimTecT CSoft Computing in Computer Graphics, Imaging, and Vision SCCGIV CSpring Conference on Computer Graphics SCCG CThe Conference on visualization of information SEE CVision Interface VI CVisMasters Design Modelling and Visualization Conference DMVC CVisual Analytics VA CVisual Information Communications International VINCI CVisualisation in Built Environment BuiltViz CVisualization In Science and Education VISE CVisualization in Software Engineering SEViz CVisualization in Software Product Lines Workshop VisPLE CWeb Visualization WebViz CWorkshop on Hybrid Intelligent Systems WHIS C。

人工智能人社考试题及答案

人工智能人社考试题及答案一、单选题（每题2分，共10题，满分20分）1. 人工智能的英文缩写是？A. AIB. IAC. AIID. AIO答案：A2. 人工智能之父是哪位科学家？A. 艾伦·图灵B. 约翰·麦卡锡C. 马文·闵斯基D. 艾伦·纽厄尔答案：B3. 下列哪个算法不是机器学习算法？A. 决策树B. 支持向量机C. 线性回归D. 牛顿迭代法答案：D4. 在人工智能领域，神经网络的灵感来源于？A. 计算机电路B. 人脑结构C. 互联网D. 基因遗传答案：B5. 深度学习在哪个领域取得了显著的进展？A. 语音识别B. 图像识别C. 自然语言处理D. 所有选项答案：D6. 人工智能中的“机器学习”与“深度学习”的主要区别是什么？A. 机器学习使用浅层神经网络，而深度学习使用深层神经网络B. 机器学习需要大量标注数据，而深度学习不需要C. 机器学习是深度学习的一个子集D. 深度学习是机器学习的一个子集答案：A7. 人工智能的三大支柱技术是什么？A. 算法、数据、硬件B. 算法、软件、硬件C. 数据、软件、硬件D. 算法、数据、网络答案：A8. 以下哪个不是人工智能的应用领域？A. 医疗诊断B. 自动驾驶C. 客户服务D. 传统手工艺答案：D9. 人工智能在未来发展中面临的最大挑战是什么？A. 技术难题B. 伦理和法律问题C. 资金投入D. 人才短缺答案：B10. 人工智能的发展目标是什么？A. 替代人类工作B. 提高生产效率C. 增强人类智能D. 所有选项答案：D二、多选题（每题3分，共5题，满分15分）1. 人工智能的主要研究领域包括哪些？A. 机器学习B. 自然语言处理C. 计算机视觉D. 机器人技术答案：ABCD2. 以下哪些是人工智能的应用实例？A. 智能客服B. 推荐系统C. 语音助手D. 无人驾驶汽车答案：ABCD3. 人工智能在医疗领域的应用包括哪些？A. 辅助诊断B. 药物研发C. 患者监护D. 手术机器人答案：ABCD4. 人工智能在教育领域的应用包括哪些？A. 个性化学习B. 智能辅导C. 虚拟助教D. 在线评估答案：ABCD5. 人工智能面临的挑战包括哪些？A. 数据隐私问题B. 算法偏见问题C. 伦理道德问题D. 安全性问题答案：ABCD三、判断题（每题1分，共5题，满分5分）1. 人工智能的发展完全依赖于大数据。

通用人工智能与机器人学习

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大大框架是对的，但是神经网网络结构太简单了了，使得这个 “大大脑”的学习水水平受到了了限制。通过进一一步改进神经网网络的结构将有可能使智能水水平大大幅度提高高改变Critic对Actor的训练方方式也会有大大的变化。
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DL
最前沿的机器器人人学习
RL
领军人人物
Pieter Abbeel OpenAI，UC Berkerley
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明年年11月月，第一一届机器器人人学习顶会
希望大大家对机器器人人学习感兴趣，并投入入其中！ Maybe it is the best opportunity !
谢谢大大家
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UNREAL算法评价
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通过多种面面向同一一个最终目目标的任务来提升Actor的水水平，符合人人类的学习方方式本质上可以认为是有多个Critic来引导Actor的训练但如何有效的定义辅助任务是一一个问题，面面向不不同的场景恐怕不不能都适用用，比比如像素控制。
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当前DRL算法的情况
Sergey Levine UC Berkerley
Raia Hadsell DeepMind
Abhinav Gupta CMU
Vincent Vanhoucke Google Brain
学术先锋
Google首首席机器器人人领导核心心
1 End-to-End Training of Deep Visuomotor Policies
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将DRL第一一次用用在机器器人人视觉导航上，彻底颠覆了了以往的机器器人人控制方方法。使用用A3C进行行行训练采用用Siamese Network连体网网络
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4 Deep Reinforcement Learning for Robotic Manipulation

介绍机器狗的英语作业作文

As a high school student with a keen interest in robotics and technology, I recently had the opportunity to delve into an intriguing project: the creation of a robotic dog. This endeavor was not only a fascinating journey into the world of artificial intelligence and mechanical engineering but also a testament to the rapid advancements in technology that are shaping our future.The project began with a comprehensive research phase. I spent countless hours poring over articles and scientific papers, trying to understand the complexities of robotic movement, artificial intelligence, and the various sensors that could be integrated into the machine. The goal was to design a machine dog that could perform basic tasks such as walking, running, and even responding to voice commands.The design process was both challenging and exhilarating. I started by sketching out the basic structure of the robot, considering the placement of the motors, sensors, and the overall aesthetic. The choice of materials was crucial it had to be lightweight yet sturdy enough to withstand the rigors of movement. I opted for a combination of aluminum and highstrength plastics, which offered the perfect balance of durability and weight.Once the design was finalized, the next step was to bring the machine dog to life. This involved programming its artificial intelligence, which was arguably the most complex aspect of the project. I utilized Python, a versatile programming language known for its simplicity and readability, to code the machines brain. The AI had to be capable of processing sensoryinputs, making decisions, and controlling the robots movements accordingly.The integration of sensors was another critical component. I equipped the machine dog with a variety of sensors including ultrasonic, infrared, and touch sensors. These sensors allowed the robot to navigate its environment, avoid obstacles, and even respond to physical touch. The ultrasonic sensors, for instance, emitted sound waves that bounced off objects and returned to the sensor, allowing the robot to calculate distances and avoid collisions.The construction phase was a meticulous process that required precision and attention to detail. Each component had to be assembled with care, ensuring that the motors were aligned correctly and the sensors were positioned accurately. The wiring had to be neat and secure, with no loose connections that could potentially cause malfunctions.After several weeks of hard work, the machine dog was finally ready for testing. The first trial was both nervewracking and exciting. I powered up the robot and watched as it came to life, its motors whirring and sensors glowing. To my relief, it began to move, albeit a bit clumsily at first. Over time, with further finetuning and programming adjustments, the machine dog became more agile and responsive.One of the most rewarding aspects of the project was observing the machine dogs interactions with the environment. It was fascinating to see how it navigated around obstacles, responded to voice commands, andeven displayed a certain level of autonomy. The project also sparked numerous discussions among my peers and teachers about the ethical implications of creating such advanced machines and their potential impact on society.In conclusion, the experience of creating a machine dog was a profound learning opportunity that expanded my understanding of robotics, artificial intelligence, and the potential of technology to shape our world. It was a project that challenged my problemsolving skills, patience, and creativity, and I am grateful for the chance to have been a part of it. As we continue to push the boundaries of what is possible with technology, I am excited to see where this journey will take us and the innovations that will emerge in the future.。

人工智能训练师培训教材和考试题库

人工智能训练师培训教材和考试题库随着人工智能（Artificial Intelligence，AI）的快速发展，对于专业的人工智能训练师的需求也越来越大。

人工智能训练师是指具备深入了解和熟练运用人工智能技术的专业人士，他们能够培训和指导其他人员在人工智能领域中进行学习和研究。

为了培养出优秀的人工智能训练师，编写一套系统完备的教材和考试题库是至关重要的。

人工智能训练师的培训教材应该包含以下几个方面的内容：1. 人工智能基础知识：教材的第一部分应该包括人工智能的基本概念、发展历程、技术原理等基础知识。

这些内容能够帮助学员快速了解人工智能的基本概念和背景，为后续的学习打下坚实的基础。

2. 人工智能算法与模型：教材的第二部分应该深入讲解人工智能的核心算法和常用模型。

包括但不限于机器学习、深度学习、自然语言处理等方面的内容。

这些算法和模型是人工智能训练师必备的核心知识，能够帮助学员掌握人工智能的实践应用。

3. 人工智能应用领域：教材的第三部分应该介绍人工智能在不同领域的应用案例和实践经验。

例如，人工智能在医疗、金融、交通等领域的应用。

这些案例能够帮助学员了解人工智能的实际应用场景，培养他们在不同领域中的应用能力。

4. 人工智能教学方法与技巧：教材的最后一部分应该涉及人工智能训练师的教学方法和技巧。

包括如何设计培训课程、如何组织讲座、如何引导学员进行实践等方面的内容。

这些教学方法和技巧能够帮助学员成为一名优秀的人工智能训练师，能够有效地传授知识和指导学员。

除了培训教材，一个完备的人工智能训练师考试题库也是必不可少的。

考试题库应该覆盖教材中的各个章节，并包含选择题、判断题、应用题等不同类型的题目。

这些题目应该能够考察学员对于人工智能基础知识、算法与模型、应用领域以及教学方法与技巧的理解和掌握程度。

考试题库的设计应该注意以下几点：1. 题目的难度分布：考试题库应该根据培训课程的难度设置题目的难度分布。

包括简单题、中等题和难题。

人工智能训练师题库

人工智能训练师题库人工智能（Artificial Intelligence，简称AI）作为一项前沿技术，正日益渗透到各个领域。

为了培养人工智能领域的专业人才，许多机构和企业开设了人工智能训练师课程，以提供专业的知识和实践经验。

而作为人工智能训练师，拥有一套题库是必不可少的，它能帮助我们检验学员的学习成果、提高培训效果。

下面是一些与人工智能训练师相关的题目，供大家参考。

1. 请简要介绍人工智能的定义和基本概念。

2. 人工智能有哪些主要的学派和方法论？3. 请解释一下人工智能的核心技术——机器学习。

4. 人工智能中的深度学习是什么？它与传统机器学习的区别是什么？5. 请列举一些人工智能领域的常用算法，以及它们的应用。

6. 请简述人工智能在自然语言处理（Natural Language Processing，简称NLP）中的应用和挑战。

7. 人工智能在图像识别和计算机视觉领域的应用有哪些？8. 请简要描述人工智能在医疗领域的应用，并提出其中的一些挑战。

9. 人工智能伦理是人工智能领域的一个重要议题，请谈谈你对人工智能伦理的理解。

10. 请简要介绍人工智能在智能交通领域的应用和未来发展趋势。

11. 请谈谈你对人工智能发展前景的看法，以及对人工智能训练师的培养建议。

以上是一些人工智能训练师题库的题目，通过回答这些问题，我们能够全面了解人工智能的基本概念、核心技术和应用领域。

人工智能的发展前景广阔，但同时也面临许多挑战，比如伦理问题、数据隐私和安全性等。

作为人工智能训练师，我们应该注重培养学员的综合能力，不仅要掌握人工智能的理论知识，还要具备良好的伦理意识和创新能力。

为了帮助学员更好地掌握人工智能的知识，我们还可以组织实践项目、参加竞赛、开展讨论和研讨会等活动，提高学员的实际应用能力和解决问题的能力。

另外，定期更新和完善题库也是十分必要的，以跟上人工智能领域的最新发展和研究成果。

总而言之，人工智能训练师题库是培养人工智能专业人才的重要工具，通过解答题目，学员能够巩固和应用所学的知识，同时也能够检验和评估他们的学习成果。

人工智能经典教材

人工智能经典教材
人工智能经典教材是指那些在人工智能领域最具影响力和历史意义的教材。

这些教材不仅涵盖了人工智能领域的核心理论和算法，还介绍了该领域的最新发展和应用。

以下是一些经典的人工智能教材：
《人工智能：现代方法》（Artificial Intelligence: A Modern Approach）：由斯坦福大学的Peter Norvig和Stuart Russell合著的这本书已成为人工智能领域的标准参考书。

它介绍了人工智能的基本概念、搜索算法、机器学习、自然语言处理和机器人等方面的知识。

《机器学习》（Machine Learning）：这本由Tom Mitchell编写的教材是机器学习领域的经典之作。

它介绍了机器学习的基本概念、分类、回归、聚类、强化学习等方面的知识，并包含了大量的案例和算法。

《统计学习方法》：这是一本由李航编写的机器学习教材，也是国内外机器学习领域广受欢迎的一本书。

它介绍了机器学习的统计学习方法的基本概念、算法和应用，并包含了大量的例子和练习。

《深度学习》（Deep Learning）：这本由Ian Goodfellow、Yoshua Bengio和Aaron Courville合著的书是深度学习领域的经典之作。

它介绍了深度学习的基本概念、神经网络、卷积神经网络、循环神经网络等方面的知识，并包含了大量的案例和算法。

以上这些经典的人工智能教材都是不可多得的宝贵资源，对于
学习人工智能领域的同学和研究人员来说都具有重要的参考价值。

人工智能的10个重大数理基础问题

人工智能的10个重大数理基础问题在深入探讨人工智能（本人）的10个重大数理基础问题之前，有必要先对人工智能的概念进行简要介绍。

人工智能是一种模拟人类智能的技术，通过计算机系统来执行类似于人类智能的任务。

这些任务包括学习、推理、问题解决和语言识别。

在过去的几十年中，人工智能已经成为了计算机科学和工程领域中最受关注和研究的领域之一。

1. 通用人工智能（AGI）的挑战人工智能的10个重大数理基础问题中，首先需要探讨的是通用人工智能（AGI）的挑战。

通用人工智能是指一种可以像人类一样执行各种智能任务的人工智能系统。

目前的人工智能系统往往只能执行特定的任务，例如语音识别、图像识别或自然语言处理。

要实现通用人工智能，需要解决诸多挑战，包括对人类智力的深刻理解、对自然语言的高度理解以及对情境的识别和推理能力等。

2. 人工神经网络的发展与优化人工神经网络是人工智能领域的核心技术之一。

它模拟人脑中神经元之间的连接，并通过层层传递信息来实现学习和推理。

在人工智能的10个重大数理基础问题中，人工神经网络的发展与优化是一个重要的课题。

如何构建更加复杂和高效的神经网络结构，如何提高神经网络的学习速度和准确度，以及如何解决过拟合和欠拟合等问题，都是当前亟待解决的问题。

3. 深度学习的理论与应用深度学习作为人工智能领域的热门技术，已经在语音识别、图像识别、自然语言处理等领域取得了巨大的成功。

然而，深度学习的理论基础仍然存在很多挑战，如深度神经网络模型的可解释性、深度学习算法的鲁棒性等问题需要进一步研究和探讨。

4. 强化学习的理论与实践强化学习是一种通过代理（Agent）与环境进行交互，从而学习最优行为策略的机器学习方法。

在人工智能的10个重大数理基础问题中，强化学习的理论与应用是一个重要的课题。

如何解决强化学习中的探索与利用之间的平衡、如何处理延迟反馈和稀疏奖励等问题，都是当前亟待解决的问题。

5. 非监督学习与自监督学习非监督学习和自监督学习是两种重要的学习范式，它们可以从无标注的数据中学习表示和特征，为人工智能系统提供更加丰富和鲁棒的学习能力。

agi发展历程

AGI（Artificial General Intelligence）是一种旨在实现通用智能的人工智能技术。

其发展历程可以追溯到上世纪50年代，当时科学家们开始探索如何模仿人类智能的方法。

以下是AGI 发展历程的主要阶段：1. 起步阶段（1950-1970年代）：这一时期，科学家们主要关注人工智能领域的基础研究，开发出了各种基于规则和符号推理的技术，如专家系统、定理证明器等。

这些技术虽然取得了一定的进展，但它们缺乏适应性和学习能力，难以处理复杂的现实世界问题。

2. 挫折阶段（1980-2000年代）：在这一时期，人工智能的发展遭遇了挫折，主要是由于计算能力的限制和数据集的缺乏。

尽管研究者们不断尝试改进算法和数据结构，但通用智能的实现仍然是一个巨大的挑战。

3. 重新崛起阶段（2000年代至今）：随着深度学习和大数据技术的发展，人工智能领域取得了显著的进步。

尤其是自然语言处理和计算机视觉技术，已经可以在许多任务上与人类表现相当或超越人类。

这些进步为AGI的实现提供了更多的可能性。

在此期间，AGI领域的研究者们不断探索新的技术和方法，包括神经网络、强化学习、深度强化学习等。

同时，随着互联网和社交媒体的普及，人们积累了大量的数据和知识，这为AGI的发展提供了丰富的资源。

此外，一些组织和企业也开始关注AGI的发展，并投入大量资源进行研究和开发。

例如OpenAI、DeepMind、DeepMind等组织和企业致力于推动AGI的发展，并与其他研究者合作，共同探索AGI的实现方法和技术。

总之，AGI的发展历程经历了多次起伏和变革，目前正处于重新崛起阶段。

未来随着技术的不断进步和资源的投入，AGI的实现将越来越有可能成为现实。

请注意，AGI的实现是一个复杂而漫长的过程，需要大量的研究和探索。

尽管取得了显著的进展，但仍存在许多挑战和难题需要解决。

未来的发展将取决于技术进步、资源投入、社会接受度等多个因素的综合影响。

人工智能训练师三级题库

人工智能训练师三级题库
人工智能训练师的资格认证通常由相关行业协会或机构负责，不同的认证机构可能有不同的考试内容和标准。

可以按以下途径获取：
1.官方渠道：访问相关认证机构的官方网站，他们通常会提供考试大纲、样题或题库，以帮助考生了解考试内容。

2.培训机构：有一些培训机构可能提供相关认证考试的培训课程，并可能包含模拟题库或考试题目。

3.社群与论坛：参与人工智能领域的社群、论坛，向有相关经验的从业者请教，可能能获取一些有关考试的信息。

4.图书资料：有关认证的考试参考书籍通常也会包含一些样题和考试经验。

请记得定期检查认证机构的官方网站，因为考试大纲和题库可能会根据行业的发展而进行更新。

人工智能知识及案例解析

3
运动控制
机器人根据内外部环境的变化自动调节运动状态。典型应用：人形机器人行走
4
机器学习
计算机基于大数据与算法模型，利用逻辑推理、数据统计与计算，找出数据的内在关系，实现对未知数据的推测。典型应用：Alpha Zero
二、人工智能教育
教育总目标：立德树人，培养符合未来人工智能社会需求的创新人才。
相结合的高斯过程
1997年Freund等提出 Adaboost算法提高弱分类
算法准确度
深度学习强化学习 2000
迁移学习联邦学习
2006年Hinton 提出了深度信念网络开启了深度学习研究热潮
2003年LeCun等提出将卷积神经网络用
于图像处理与识别
2007年Bengio提出堆叠自动编码器模型
STEP4 创意
头脑风暴，收集并记录尽可能多的想法。此阶段遵循以下六条原则： 1、暂缓评论 2、异想天开 3、不要跑题 4、一次一人发言 5、图文并茂 6、多多益善在此环节必须注意聆听，注重以图画的方式展示和记录创意想法。
注：头脑风暴六条原则选自《IDEO，设计改变一切》
STEP5 计划
对上一环节收集的创意，结合项目需求和限制条件，遴选出最合适的方案，制定可执行的项目推进计划。团队需要把计划分解为若干子任务，明确每个子任务/阶段的负责人、时间节点与验收标准。
STEP2 组队分工
姓名
擅长
张三
思维活跃鬼点子多
李四
组织能力强善于沟通
赵五陈六
技术好
审美能力强语文功底好
任务
信息收集创意整理
队长制定计划考核进度鼓舞士气交流展示
程序设计验证调试

几种智力模型理论

几种智力模型理论智力模型理论智力的本质是什么目前心理学界并没有达成共识。

自20世纪初，心理学家们从各种不同的角度对人的智力提出假设，进行了广泛的研究，形成了众多不同的理论，但从总体上看，我们可以把这些智力理论基本上分为智力的因素理论和智力的认知理论两大派别。

在这两大派别中，吉尔福特的智力结构理论、斯腾伯格的智力成功智力理论、加德纳的多元智力理论和戴斯德PASS理论都较具代表性。

下面我们向大家做一些介绍。

美国心理学家吉尔福特的智力结构理论是因素理论中的重要理论。

智力因素理论又称智力的测量理论，是以因素分析方法为基础建立的，即分析出组成智力的因素，关注各个因素的发展，从而使智力的测量和找出个别差异成为可能，为个性化实施教育提供依据。

美国心理学家吉尔福特于1967年提出智力是由120个独立的因素组成。

吉尔福特按三个维度组织这些因素，以这三个维度的相互作用来决定不同的人的智力能力。

吉尔福特智力结构理论不仅为我们今天的智力训练提供了可操作性的依据，国内外现有很多根据这一理论开展的各种思维训练教材和课程;而且，它对创造力的阐述也为后人提供了重要的测量和训练的基础。

吉尔福特认为创造性包括思维的流畅性、变通性和独创性等，这几种能力在他的三维智力结构模型中都可以找到相对应的智力因素，对这些因素进行相应的教育训练，则可以促进儿童创造力的发展。

20世纪下半叶认知心理学兴起后，人们对智力的研究出现了另一条研究途径，即信息加工途径。

智力认知理论主要受认知心理学的信息加工理论及神经生物学(脑科学)的影响，它们对智力的因素组成不再斤斤计较，而是关注信息加工的过程。

他们探讨的问题是：为了解答某种智力任务，必须经历哪些心理操作，测验成绩的哪些方面取决于过去的学习，哪些方面取决于注意、短时记忆或信息加工速度等。

斯腾伯格是美国耶鲁大学心理学教授。

他从信息加工心理学的角度，于1986年提出了智力的三元理论，认为智力包括成分智力、经验智力和情境智力。

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Introduction
The paper is organized as follows: we rst review the ideas behind the Spatial Semantic Hierarchy SSH as well as we present the language of Causal theories. In particular, we de ne the language in which the topological map is described. Then we present our theory describing how the agent assimilates boundary relations. Finally, we de ne the boundary relations entailed by the environment given the set of actions executed by the agent. In this section we describe the main ideas behind the Spatial Semantics Hierarchy SSH as well as the language of Causal theories McCain & Turner 1997. We describe in detail the SSH topological level as we are interested in de ning how boundary regions are associated with it. Causal theories will be used then to formally specify how boundary relation are established.
We are interested in the problem of how an agent organizes its sensorimotor experiences in order to create a spatial representation. Our approach to solve this problem is the Spatial Semantic Hierarchy SSH, where multiple levels of spatial representation coexists. At the SSH topological level, space is represented by places and connectivity relation among them. Places are arranged into streets so that the topological representation looks like the street network of a city. Grouping places into regions allows an agent to reason e ciently about its spatial knowledge. Different types of regions can be de ned as the agent travels in the environment. Using the language of Causal Theories, we give a formal account of how an agent establishes boundary region relations while navigating its environment.1
Abstract
are arranged into streets so that the topological map2 looks like the street network of a city. When people solve route nding problems using a map, they group places into regions. Regions are then used to guide the search for a route between two speci c places. For example, in order to nd a route from Austin to Boston, we might rst nd a route from Texas to Massachusetts, and then use this route to nd the actual route from Austin to Boston. In order for an autonomous agent to use this hierarchical planning strategy, it has to create the appropriated space representation from its sensorimotor experiences. In this paper we describe how an agent establishes boundary region relations while navigating its environment see footnote 1. Once a su cient number of boundary relations have been accumulated, they provide a useful topological route- nding heuristic. For example, to nd a route from A to B, if there exists a street s such that A belongs to the right of s and B belongs to the left of s, look for routes from A to s and from s to B. The idea of using boundary relations in the context of the SSH was informally proposed in Kuipers 1978; Kuipers & Levitt 1988. In this paper we give a formal ground to those ideas. Using the formalism of causal theories McCain & Turner 1997 we describe how an agent deduces di erent boundary relations while navigating its environment. As it will be computationally expensive and cognitively ungrounded to assume that the agents knows the relation between every place and every boundary, we are interested in de ning the di erent states of partial knowledge associated with boundary relations. Moreover, as we do not rely on metrical information, our formalization captures the following default: in order for an agent to go from one side to the other of a boundary, the agent has to cross that boundary. We analyze how the boundary relations are a ected when this default is not true, that is, when the agent misses the boundary.
2 We use the term topological map to refer t
The basic problem we are interested in solving is how an agent creates its spatial representation from its sensorimotor experiences. Our approach to solve this problem is the Spatial Semantic Hierarchy SSH Kuipers & Byun 1988; Kuipers et al. 1993; Kuipers 1996; Kuipers & Byun 1991; Kuipers 1978; Kuipers & Levitt 1988. The SSH is an ontological hierarchy, where each level of the hierarchy has its own ontology abstracting the ontology of the levels below it. In this paper we are primarily concerned with the SSH topological level. At this level, space is represented by places and connectivity relations among them. Places
In Cognitive Robotics 1998 AAAI Fall Symposium AAAI Tech Report FS-98-02, 1998.

In Cognitive Robotics 1998 AAAI Fall Symposium

机器人技术二级理论考点

马文明斯基

Insight Problem Solving A Critical Examination of the Possibility

人工智能训练师培训教材和考试题库

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