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Chapter 17
Learning
323-670 Artificial Intelligence
ดร.วิภาดา เวทย์ ประสิทธิ์ ภาควิชาวิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์ มหาวิทยาลัยสงขลานครินทร์
Learning
1. Rote learning
2. Learning by taking advice
3. Learning by problem solving
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Parameter adjustment
Macro-Operators
4. Learning from examples
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Induction : Winston’s learning program p.458
Version Spaces : Candidate eliminate algorithm
Decision tree
5. Explanation-based learning p 482
6. Formal learning theory
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Winston’s learning program
Concept : P.459
1. Begin with a structural description of one known instance of the
concept. Call the description the concept definition.
2. Examine descriptions of other known instances of the concepts.
Generalize the definition to include them.
3. Examine descriptions of near misses of concept, Restrict the
definition to exclude these.
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Winston’s learning program p.458
Block world concept : Figure 17.2 p. 459
Structure description : Figure 17.3 p. 460
The comparison of two arches : Figure 17.4 p. 461
The arch description after two examples : Figure 17.5 p. 462
The arch “description after a near miss : Figure 17.6 p. 463
 use semantic networks to describe block structures
 use matching process to detect similarities and differences between
structures
 use isa hierarchy to describe relationship among already known objects
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Version Spaces
 The goal : to produce a description that is consistent with all positive
examples but no negative examples in the training set.
 use frame representing concept for car see Figure 17.7 p. 463
 Features/Slots : { value1, value2,...,valueN }
– origin : { Japan, USA, Britain }
 Variables : X1, X2, X3
 concept space : see Figure 17.11 Concept of Version Spaces p. 466
variables
target concept
all training instance
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Version Spaces
 version space = current hypothesis = subset of concept space = largest collection
of descriptions that is consistent with all the training examples seen so far.
 concept space = G or S
 G = contain the most general descriptions consistent with the training example seen
so far.
 S = contain the most specific descriptions consistent with training exaples
 positive example (+)  move S to more specific
 negative example (-)  move G to more specific
 if G and S sets converge  the hypothesis is a single concept description
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Version Spaces
 Candidate Eliminate Algorithm p.466-467
 algorithm that use to narrow down the version space
 by remove any descriptions that are inconsistent with set G and set S
 Car Example
– Figure 17.7 Concept Car : p. 463
– Figure 17.8 Representation language for car : p. 464
– Figure 17.9 The concept Japanese car : p. 464
– Figure 17.10 Partial ordering of concepts : p. 465
– Figure 17.12 Positive and negative examples of car : p. 467
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Version Spaces
 We want “Japanese economy car”
 From Figure 17.12 Positive and negative examples of car : p. 467
 [origin = X1, manufacture = X2, color = X3, decade = X4, type = X5]
 GET EX1 (+) G = {(X1, X2, X3, X4, X5)}
S = {(Japan,Honda, Blue,1980,Economy}) =Figure 17.12 in EX1
 GET EX2 (-) G = {(X1, Honda, X3, X4, X5), (X1, X2, Blue, X4, X5) ,
(X1, X2, X3, 1980, X5), (X1, X2, X3, X4, Economy)}
S = {(Japan,Honda, Blue,1980,Economy}) ** the same because (-) example
 GET EX3 (+) check G first, G = {(X1, X2, Blue, X4, X5) ,(X1, X2, X3, X4, Economy)}
S = {(Japan,X2, Blue,X4,Economy})
 GET EX4 (-) check G first, G = {(Japan, X2, Blue, X4, X5) ,(Japan, X2, X3, X4, Economy)}
S = {(Japan,X2, Blue,X4,Economy}) ** the same because (-) example
 GET EX5 (+) check G first, G = {(Japan, X2, X3, X4, Economy)}
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S = {(Japan,X2, X3,X4, Economy})
Version Spaces
 Note : The algorithm is least commitment algorithm : produce as little as possible at
each step
 Problems
– 1.) S and G may not converge to a single hypothesis
– 2. ) if there is a noise (inconsistent data)  the algorithm will be premature,
we may prune the target concept too fast
* For example if the data number three given the negative sign (-) instance of
positive sign (+) ... no matter how much the data is we can not find the
concept....
* How to fix this problem is to maintain several G and S sets
BUT it is costly and may have the bounded inconsistency problem
– 3.) We can not use OR in the questions ask
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* For
example
: Italian
sport car or German luxury
car”
Decision Tree
 ID3 Program = to classify a particular input, we start at the top of the tree and answer
questions until we reach a leaf, where the classification is stored. See Figure 17.13
Decision tree p. 470
 1. Choose window = random subset of training examples to train
 2. Outside window = use to test the decision tree
 3. Use empirical evidence (iterative method) to build up decision tree
 4. Building a node = choosing some attribute to divide training instance into subset
consider (+) sign
 Can use with OR .... just change (-) sign into (+) sign
 Problems : noisy input, attribute value may be unknown, may have l;arge decision
tree and hard to understand relationship See Figure 17.13
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Explanation-Based Learning
 provide explanation
 depend on domain theory/
domain knowledge
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Formal Learning Theory
 Given positive and negative examples
 produce algorithm that will classify future examples
correctly with probability 1/h
 Complexity of learning :
– the error tolerance (h)
– the number of binary features present in the examples (t)
– the size of the rule necessary to make the discrimination (f)
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Formal Learning Theory
 if the number of training examples required is polynomial
in h,t, and f  then the concept is learnable.
 few training examples are needed  learnable
 we restrict the learner to the positive examples only.
 See Figure 12.22 Concept of elephant P. 483
 elephant = “gray, mammal, large”
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Induction
induction : A method of reasoning by which one infers a generalization
from a series of instances. emphasis to all BEANS : all instances
Inductive syllogisms are of the following form:
•
•
•
1. These beans are from this bag. (and these
beans..., and these beans..., etc.)
2. These beans are (all) white.
# 3 Therefore, all beans from this bag are white.
In a much broader sense, induction can be thought to include various other
forms of reasoning including analogical reasoning, inference to cause
form symptoms, and confirmation of laws and theories.
1
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Deduction
deduction - A method of reasoning by which one infers a conclusion
from a set of sentences by employing the axioms and rules of
inference for a given logical system.
Use the term 'deduction' in a general sense to denote the fact that a
conclusion follows necessarily from the premises.
Deductive syllogisms in quantificational predicate calculus are of the
1
following form:
»
»
1. All beans from this bag are white....
2. These beans are from this bag.
»
#4 Therefore, these beans are white.....
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emphasis to one BEAN : one instance
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Abduction
abduction -A method of reasoning by which one infers to the ......best
explanation.....
- A heuristic procedure that reasons inductively from available
empirical evidence to the discovery of the probable hypotheses that
would best explain its occurrence.
Abductive syllogisms are of the following form:
#3 All beans from this bag are white
#4 These beans are white.
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