Expert Systems

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Lucas, P.J.F. (2005)
Expert systems mimic the problem-solving activity of human experts in specialized domains by capturing and representing expert knowledge. Expert systems include a knowledge base, an inference engine that derives conclusions from the knowledge, and a user interface. Knowledge may be stored as if-then rules, orusing other formalisms such as frames and predicate logic. Uncertain knowledge may be represented using certainty factors, Bayesian networks, Dempster-Shafer belief functions, or fuzzy sets. Methods of knowledge acquisition include interviewing, analysis of past records of expert decisions, and observation of experts engaged in their natural activity. An expert system shell is a commercially available programming environment that allows the entry of domain knowledge in the form of rules. Expert system shells enable end-user computing, where skilled professionals, such as engineers and scientists, can encode their own knowledge into software. Expert systems are widely used in businesses to perform tasks ranging from diagnosis of manufacturing processes to credit approval by credit card companies. Wiley Online library
  • References (5)

    1 Introduction 6 1.1 De nition of the Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2 Origin and Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

    2 Expert System Principles 7 2.1 Expert System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Problem-solving Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

    3 Knowledge Representation and Inference 9 3.1 Logic Representation and Reasoning . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.1 Horn-clause Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.2 Objects, Attributes and Values . . . . . . . . . . . . . . . . . . . . . . 12 3.2 Diagnostic Problem Solving . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2.1 Deductive Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2.2 Abductive Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2.3 Consistency-based Diagnosis . . . . . . . . . . . . . . . . . . . . . . . 15 3.3 Con guring and Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.4 Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.5 Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

    [17] Schreiber A.Th., Akkermans H., Anjewierden A., De Hoogh R., Shadbolt N., Van de Velde W., Wielinga B. (2000). Knowledge Engineering and Management: The CommonKADS Methodology. MIT Press, Menlo Park, CA. [Book presenting an overview of the CommonKADS knowledge-engineering methodology.]

    [18] Shachter R.D. (1986). Evaluating in uence diagrams. Operation Research, vol. 34, no. 6, pp. 871{882. [First paper proposing an algorithm to manipulate in uence diagrams for the purpose of decision making.]

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