Knowledge Representation 知识表示
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Summary
Knowledge Representation?
Ambiguous term
“The study of how to put knowledge into a form that a computer can reason with” (Russell and Norvig)
Problems
Does not capture temporal relations Does not handle probabilistic facts Does not handle facts w/ degrees of truth
Has been extended to:
3 specific examples
Artificial Neural Networks (ANN) Bayesian Networks Reinforcement Learning
Artificial Neural Networks (ANN)
1st work in AI (McCulloch & Pitts, 1943) Attempt to mimic brain neurons Several binary inputs, One binary output
Blockworlds (1972)
SHRDLU
“Find a block which is taller than the one you are holding and put it in the box”
Early Work - Theme
Limit domain
“Microworlds” Allows precise rules
x y Brother(x,y) Sibling(x,y) x y Loves(x,y)
Can capture lots of commonsense knowledge
Not a cure-all
First order Logic - Problems
Faithful captures fact, objects and relations
Single Layer feed-forward ANNs (Perceptrons)
N
O RnIn threshold n0
Can be chained together to
Represent logical connectives (and, or, not) Compute any computable functions
Hebb (1949) introduced simple rule to modify connection strength (Hebbian Learning)
Originally couple w/ linguistics Lead to philosophical analysis of
language
Knowledge Representation?
Cool Robots Futuristic Robots
Early Work
SAINT (1963)
Temporal logic Probability theory Fuzzy logic
First order Logic - Bigger Problem
Still lots of human effort “Knowledge Engineering”
Time consuming Difficult to debug
Closed form Calculus Problems x2 x
STUDENT (1967)
“If the number of customers Tom gets is twice the square of 20% of the number of advertisements he runs, and the number of advertisements he runs is 45, what is the number of customers Tom gets?
Size still a problem Automated acquisition of knowledge
is important
Machine Learning
Sidesteps all of the previous problems
Represent Knowledge in a way that is immediately useful for decision making
Knowledge Representation and Machine Learning
Stephen J. Guy
Overview
Recap some Knowledge Rep.
History First order logic
Machine Learning
ANN Bayesian Networks Reinforcement Learning
Generality Problem Size
1) Making rules are hard 2) State space is unbounded
Generality
First-order Logic
Is able to capture simple Boolean relations and facts
Inputs: I1, I2, …
Responses: R1, R2, …
Output: O
N
O RnIn threshold n0
Artificial Neural Networks (ANN)
Inputs: I1, I2, …
Respput: O
Knowledge Representation?
Ambiguous term
“The study of how to put knowledge into a form that a computer can reason with” (Russell and Norvig)
Problems
Does not capture temporal relations Does not handle probabilistic facts Does not handle facts w/ degrees of truth
Has been extended to:
3 specific examples
Artificial Neural Networks (ANN) Bayesian Networks Reinforcement Learning
Artificial Neural Networks (ANN)
1st work in AI (McCulloch & Pitts, 1943) Attempt to mimic brain neurons Several binary inputs, One binary output
Blockworlds (1972)
SHRDLU
“Find a block which is taller than the one you are holding and put it in the box”
Early Work - Theme
Limit domain
“Microworlds” Allows precise rules
x y Brother(x,y) Sibling(x,y) x y Loves(x,y)
Can capture lots of commonsense knowledge
Not a cure-all
First order Logic - Problems
Faithful captures fact, objects and relations
Single Layer feed-forward ANNs (Perceptrons)
N
O RnIn threshold n0
Can be chained together to
Represent logical connectives (and, or, not) Compute any computable functions
Hebb (1949) introduced simple rule to modify connection strength (Hebbian Learning)
Originally couple w/ linguistics Lead to philosophical analysis of
language
Knowledge Representation?
Cool Robots Futuristic Robots
Early Work
SAINT (1963)
Temporal logic Probability theory Fuzzy logic
First order Logic - Bigger Problem
Still lots of human effort “Knowledge Engineering”
Time consuming Difficult to debug
Closed form Calculus Problems x2 x
STUDENT (1967)
“If the number of customers Tom gets is twice the square of 20% of the number of advertisements he runs, and the number of advertisements he runs is 45, what is the number of customers Tom gets?
Size still a problem Automated acquisition of knowledge
is important
Machine Learning
Sidesteps all of the previous problems
Represent Knowledge in a way that is immediately useful for decision making
Knowledge Representation and Machine Learning
Stephen J. Guy
Overview
Recap some Knowledge Rep.
History First order logic
Machine Learning
ANN Bayesian Networks Reinforcement Learning
Generality Problem Size
1) Making rules are hard 2) State space is unbounded
Generality
First-order Logic
Is able to capture simple Boolean relations and facts
Inputs: I1, I2, …
Responses: R1, R2, …
Output: O
N
O RnIn threshold n0
Artificial Neural Networks (ANN)
Inputs: I1, I2, …
Respput: O