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Rule-based ES
• Rules as a knowledge representation technique
• Type of rules :- relation, recommendation, directive, strategy and heuristic
Expert
Structure of a rule-based ES
Rule: IF-THEN Fact
Knowledge base Database
Inference engine
Explanation facilities
User interface Developer interface
User Knowledge engineer
Externaldatabase
External program
Structure of a rule-based ES• Fundamental characteristic of an ES
– High quality performance• Gives correct results• Speed of reaching a solution• How to apply heuristic
– Explanation capability• Although certain rules cannot be used to justify a
conclusion/decision, explanation facility can be used to expressed appropriate fundamental principle.
– Symbolic reasoning
Structure of a rule-based ES• Forward and backward chaining inference
Rule: IF A is x THEN is y
Fact: A is xFact: B is y
Knowledge base
Database
Match Fire
Conflict Resolution• Example
– Rule 1:IF the ‘traffic light’ is green
THEN the action is go
– Rule 2:IF the ‘traffic light’ is red
THEN the action is stop
– Rule 3:IF the ‘traffic light’ is red
THEN the action is go
Conflict Resolution Methods
• Fire the rule with the highest priority– example
• Fire the most specific rules– example
• Fire the rule that uses the data most recently entered in the database - time tags attached to the rules– example
Uncertainty Problem
• Sources of uncertainty in ES– Weak implication– Imprecise language– Unknown data– Difficulty in combining the views of different
experts
Uncertainty Problem
• Uncertainty in AI– Information is partial– Information is not fully reliable– Representation language is inherently imprecise– Information comes from multiple sources and it
is conflicting– Information is approximate– Non-absolute cause-effect relationship exist
Uncertainty Problem
• Representing uncertain information in ES– Probabilistic– Certainty factors– Theory of evidence– Fuzzy logic– Neural Network– GA– Rough set
Uncertainty Problem
• Representing uncertain information in ES– Probabilistic– Certainty factors– Theory of evidence– Fuzzy logic– Neural Network– GA– Rough set
Uncertainty Problem
• Representing uncertain information in ES– Probabilistic
• The degree of confidence in a premise or a conclusion can be expressed as a probability
• The chance that a particular event will occur
eventsofnumberTotal
ofoccurencethefavoringoutcomesofNumberXP )(
Uncertainty Problem
• Representing uncertain information in ES– Bayes Theorem
• Mechanism for combining new and existent evidence usually given as subjective probabilities
• Revise existing prior probabilities based on new information
• The results are called posterior probabilities
eventsofnumberTotal
ofoccurencethefavoringoutcomesofNumberXP )(
Uncertainty Problem
• Bayes theorem
– P(A/B) = probability of event A occuring, given that B has already occurred (posterior probability)
– P(A) = probability of event A occuring (prior probability)
– P(B/A) = additional evidence of B occuring, given A;– P(not A) = A is not going to occur, but another event is
P(A) + P(not A) = 1
)(*)/()()/(
))(*/()/(
AnotPAnotBPAPABp
APABPBAP
Uncertainty Problem• Representing uncertain information in ES
– Probabilistic– Certainty factors– Theory of evidence– Fuzzy logic– Neural Network– GA– Rough set
Uncertainty Problem
• Representing uncertain information in ES– Certainty factors
• Uncertainty is represented as a degree of belief
• 2 steps– Express the degree of belief
– Manipulate the degrees of belief during the use of knowledge based systems
• Based on evidence (or the expert’s assessment)
• Refer pg 74
Certainty Factors• Form of certainty factors in ES
IF <evidence>THEN <hypothesis> {cf }
• cf represents belief in hypothesis H given that evidence E has occurred
• Based on 2 functions– Measure of belief MB(H, E)– Measure of disbelief MD(H, E)
• Indicate the degree to which belief/disbelief of hypothesis H is increased if evidence E were observed
Certainty Factors• Uncertain term and their intepretation
Term Certainty Factor
Definitely not -1.0
Almost certainly not -0.8
Probably not -0.6
Maybe not -0.4
Unknown -0.2 to +0.2
Maybe +0.4
Probably +0.6
Almost certainly +0.8
Definitely +1.0
Certainty Factors• Total strength of belief and disbelief in a
hypothesis (pg 75)
)],(),,(min[1
),(),(
EHMDEHMB
EHMDEHMBcf
Certainty Factors• Example : consider a simple rule
IF A is X
THEN B is Y
– In usual cases experts are not absolute certain that a rule holds
IF A is X
THEN B is Y {cf 0.7};
B is Z {cf 0.2}
• Interpretation; how about another 10%
• See example pg 76
Certainty Factors• Certainty factors for rules with multiple
antecedents– Conjunctive rules
• IF <E1> AND <E2> …AND <En> THEN <H> {cf}
• Certainty for H is
cf(H, E1 E2 … En)= min[cf(E1), cf(E2),…, cf(En)] x cf
See example pg 77
Certainty Factors• Certainty factors for rules with multiple
antecedents– Disjunctive rules rules
• IF <E1> OR <E2> …OR <En> OR <H> {cf}
• Certainty for H is
cf(H, E1 E2 … En)= max[cf(E1), cf(E2),…, cf(En)] x cf
See example pg 78
Certainty Factors• Two or more rules effect the same hypothesis
– E.g– Rule 1 : IF A is X THEN C is Z {cf 0.8}
IF B is Y THEN C is Z {cf 0.6}
Refer eq.3.35 pg 78 : combined certainty factor
Uncertainty Problem• Representing uncertain information in ES
– Probabilistic– Certainty factors– Theory of evidence– Fuzzy logic– Neural Network– GA– Rough set
Theory of evidence
• Representing uncertain information in ES• A well known procedure for reasoning with
uncertainty in AI
• Extension of bayesian approach
• Indicates the expert belief in a hypothesis given a piece of evidence
• Appropriate for combining expert opinions
• Can handle situation that lack of information
Rough set approach
• Rules are generated from dataset– Discover structural relationships within
imprecise or noisy data– Can also be used for feature reduction
• Where attributes that do not contributes towards the classification of the given training data can be identified or removed
Rough set approach:Generation of Rules
[E1, {a, c}], [E2, {a, c},{b,c}],[E3, {a}],[E4, {a}{b}],[E5, {a}{b}]
a1c3 d1a1c1 d2,b2c1 d2a2 d2 b3 d2a3 d3,a3 d4b5 d3,b5 d4
Reducts
Equivalence Classes
Rules
Class a b c dec
E1 1 2 3 1 E2 1 2 1 2 E3 2 2 3 2 E4 2 3 3 2 E5,1 3 5 1 3 E5,2 3 5 1 4
Rough set approach:Generation of Rules
Class Rules Membership Degree
E1 a1c3 d1 50/50 = 1
E2 a1c1 d2 5/5 = 1
E2 b2c1 d2 5/5 = 1
E3, E4 a2 d2 40/40 = 1
E4 b3 d2 10/10 = 1
E5 a3 d3 4/5 = 0.8
E5 a3 d4 1/5 = 0.2
E5 b5 d3 4/5 = 0.8
E5 b5 d4 1/5 = 0.2
Rules Measurements : Support
Given a description contains a conditional part and the decision part , denoting a decision rule . The support of the pattern is a number of objects in the information system A has the property described by .
The support of is the number of object in the IS A that have the decision described by .
The support for the decision rule is the probability of that an object covered by the description is belongs to the class.
)(sup port
)(sup port
)(sup)(sup portport
Rules Measurement : Accuracy
The quantity accuracy ( ) gives a measure of how trustworthy the rule is in the condition . It is the probability that an arbitrary object covered by the description belongs to the class. It is identical to the value of rough membership function applied to an object x that match . Thus accuracy measures the degree of membership of x in X using attribute B.
)(sup
)(sup)(
port
portAccuracy
Rules Measurement : Coverage
Coverage gives measure of how well the pattern describes the decision class defined through . It is a probability that an arbitrary object, belonging to the class C is covered by the description D.
)(sup
)(sup)(
port
portCoverage