Systematic Introduction to Expert Systems: Knowledge Representations and Problem-Solving Methods
At present one of the main obstacles to a broader application of expert systems is the lack of a theory to tell us which problem-solving methods areavailable for a given problem class. Such a theory could lead to significant progress in the following central aims of the expert system technique: - Evaluating the technical feasibility of expert system projects: This depends on whether there is a suitable problem-solving method, and if possible a corresponding tool, for the given problem class. - Simplifying knowledge acquisition and maintenance: The problem-solving methods provide direct assistance as interpretation models in knowledge acquisition. Also, they make possible the development of problem-specific expert system tools with graphical knowledge acquisition components, which can be used even by experts without programming experience. - Making use of expert systems as a knowledge medium: The structured knowledge in expert systems can be used not only for problem solving but also for knowledge communication and tutorial purposes. With such a theory in mind, this book provides a systematic introduction to expert systems. It describes the basic knowledge representations and the present situation with regard tothe identification, realization, and integration of problem-solving methods for the main problem classes of expert systems: classification (diagnostics), construction, and simulation.
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Review of the ProblemSolving Type Construction 207
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additional agenda algorithm application attached procedures attributes backward chaining Bayes belief revision calculated case-comparing Chap complex concept configuration constraint propagation construction steps corresponding data abstractions data base decision tree default values derived diagnoses dialog domain domain-specific efficient evaluation evidence values example expert system tools fault models Figure first-order predicate logic formula forward chaining functional models given heuristic classification hierarchy hypotheses input intermediate interval justification KL-ONE knowledge acquisition knowledge base knowledge representation knowledge types loops MED2 MYCIN normal object types observations OPS5 output parameter values possible precondition predicate logic priori probabilities problem types problem-solving methods problem-solving types problem-specific programming languages PROLOG propose-and-revise strategy propositional calculus propositions refinement relationships represented revision rule interpreter Sect selected set-covering shown in Fig similarity simple simulation skeletal construction skeletal plan solution classes solving specific structure suitable symptoms techniques temporal Theorem tion uncertainties value range variables workpiece