publication . Part of book or chapter of book . Article . Other literature type . Book . 2011

Expert systems

Waldemar Rebizant;
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  • Published: 01 Jan 2011
  • Publisher: Springer Berlin Heidelberg
Abstract
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 exp...
Subjects
free text keywords: expert systems, artificial intelligence, knowledge, expertise, problemsolving, knowledge, rules, logic, uncertainty, Control and Systems Engineering, Mechanical Engineering, Industrial and Manufacturing Engineering, Software, Computer Science Applications, Marketing, Economics and Econometrics, Business and International Management, Library and Information Sciences, Information Systems, Expert system, computer.software_genre, computer, Software engineering, business.industry, business, Computer science, General Materials Science, Safety, Risk, Reliability and Quality, Analytical Chemistry, General Computer Science, Electrical and Electronic Engineering, Health Policy, Pharmacology, General Medicine, Law
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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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publication . Part of book or chapter of book . Article . Other literature type . Book . 2011

Expert systems

Waldemar Rebizant;