chapter05 - Chapter 5 Inexact Reasoning Expert Systems...

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Chapter 5: Inexact Reasoning Expert Systems: Principles and Programming, Fourth Edition
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Expert Systems: Principles and Programming, Fourth Edition2 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
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Expert Systems: Principles and Programming, Fourth Edition3 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
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Expert Systems: Principles and Programming, Fourth Edition4 Figure 5.1 Major Uncertainties in Rule-Based Expert Systems
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Expert Systems: Principles and Programming, Fourth Edition5 Figure 5.2 Uncertainties in Individual Rules
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Expert Systems: Principles and Programming, Fourth Edition6 Figure 5.3 Uncertainty Associated with the Compatibilities of Rules
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Expert Systems: Principles and Programming, Fourth Edition7 Figure 5.4 Uncertainty Associated with Conflict Resolution
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Expert Systems: Principles and Programming, Fourth Edition8 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.
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Expert Systems: Principles and Programming, Fourth Edition9 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.
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Expert Systems: Principles and Programming, Fourth Edition10 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.
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Expert Systems: Principles and Programming, Fourth Edition11 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.
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Expert Systems: Principles and Programming, Fourth Edition12 Uncertainty in Conflict Resolution There is uncertainty in conflict resolution with regard to priority of firing and may depend on a
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This note was uploaded on 11/10/2011 for the course PSY 101 taught by Professor N during the Spring '11 term at CUNY City Tech.

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chapter05 - Chapter 5 Inexact Reasoning Expert Systems...

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