The organization of expert systems, a tutorial

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Abstract

This is a tutorial about the organization of expert problem-solving programs. We begin with a restricted class of problems that admits a very simple organization. To make this organization feasible it is required that the input data be static and reliable and that the solution space be small enough to search exhaustively. These assumptions are then relaxed, one at a time, in case study of ten more sophisticated organizational prescriptions. The first cases give techniques for dealing with unreliable data and time-varying data. Other cases show techniques for creating and reasoning with abstract solution spaces and using multiple lines of reasoning. The prescriptions are compared for their coverage and illustrated by examples from recent expert systems.

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    There is currently much interest and activity in expert systems both for research and applications. A forthcoming book edited by Hayes-Roth, Waterman, and Lenat [21] provides a broad introduction to the creation and validation of expert systems for a general computer science audience. An extended version of this tutorial, which introduces concepts and vocabulary for an audience without an AI background, will appear as a chapter in the book.

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    Additional affiliations: J. Aikins, Hewlett-Packard, Palo Alto, CA; R. Balzer, USC/Information Sciences Institute, Marina del Rey, CA; J. Benoit, The MITRE Corporation, McLean, VA; L. Birnbaum, Yale University, New Haven, CT; F. Hayes-Roth, Teknowledge, Palo Alto, CA; E. Sacerdoti, Machine Intelligence Corp., Palo Alto, CA.

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