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Probabilistic Similarity Networks

Part of the ACM Doctoral Dissertation Award S. series
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In this blend of formal theory and practical application, David Heckerman develops methods for building normative expert systems - expert systems that encode knowledge in a decision-theoretic framework.

Heckerman introduces the similarity network and partition, two extensions to the influence diagram representation.

He uses the new representations to construct Pathfinder, a large, normative expert system for the diagnosis of lymph-node diseases.

Heckerman shows that such expert systems can be built efficiently, and that the use of a normative theory as the framework for representing knowledge can dramatically improve the quality of expertise that is delivered to the user.

He concludes with a formal evaluation of the power of his methods for building normative expert systems.

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Product Details
MIT Press
0262082063 / 9780262082068
Hardback
03/01/1992
United States
English
264 pages, Ill.
235 x 182 mm, 600 grams
Professional & Vocational Learn More