03-12-2012, 04:15 PM
Expert Systems: Principles and Programming, Fourth Edition
Expert Systems.ppt (Size: 549.5 KB / Downloads: 283)
Objectives
Explore the sources of uncertainty in rules
Analyze some methods for dealing with uncertainty
Learn about the Dempster-Shafer theory
Learn about the theory of uncertainty based on fuzzy logic
Discuss some commercial applications of fuzzy logic
Uncertainty and Rules
We have already seen that expert systems can operate within the realm of uncertainty.
There are several sources of uncertainty in rules:
Uncertainty related to individual rules
Uncertainty due to conflict resolution
Uncertainty due to incompatibility of rules
Goal of Knowledge Engineer
The knowledge engineer endeavors to minimize, or eliminate, uncertainty if possible.
Minimizing uncertainty is part of the verification of rules.
Verification is concerned with the correctness of the system’s building blocks – rules.
Verification vs. Validation
Even if all the rules are correct, it does not necessarily mean that the system will give the correct answer.
Verification refers to minimizing the local uncertainties.
Validation refers to minimizing the global uncertainties of the entire expert system.
Uncertainties are associated with creation of rules and also with assignment of values.
Ad Hoc Methods
The ad hoc introduction of formulas such as fuzzy logic to a probabilistic system introduces a problem.
The expert system lacks the sound theoretical foundation based on classical probability.
The danger of ad hoc methods is the lack of complete theory to guide the application or warn of inappropriate situations.
Sources of Uncertainty
Potential contradiction of rules – the rules may fire with contradictory consequents, possibly as a result of antecedents not being specified properly.
Subsumption of rules – one rules is subsumed by another if a portion of its antecedent is a subset of another rule.
Uncertainty
When a fact is entered in the working memory, it receives a unique timetag – indicating when it was entered.
The order that rules are entered may be a factor in conflict resolution – if the inference engine cannot prioritize rules, arbitrary choices must be made.
Redundant rules are accidentally entered / occur when a rule is modified by pattern deletion.
Difficulties with Bayesian Method
The Bayesian method is useful in medicine / geology because we are determining the probability of a specific event (disease / location of mineral deposit), given certain symptoms / analyses.
The problem is with the difficulty / impossibility of determining the probabilities of these givens – symptoms / analyses.
Evidence tends to accumulate over time.