Probabilistic Similarity Networks, Issue 1316
In this remarkable 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. David Heckerman is Assistant Professor of Computer Science at the University of Southern California. He received his doctoral degree in Medical Information Sciences from Stanford University.
Contents: Introduction. Similarity Networks and Partitions: A Simple Example. Theory of Similarity Networks. Pathfinder: A Case Study. An Evaluation of Pathfinder. Conclusions and Future Work.
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Theory of Similarity Networks
A Case Study
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ABDOMINAL PAIN Algorithm 3.1 Algorithm 3.2 applied arc from h assertions of conditional assertions of subset axiom c-local map c-local map hi-hj Chapter clusters compose comprehensive network comprehensive similarity network compute conditional independence conditionally independent consistent contains decision maker decision theory decision-theoretic definition Dempster-Shafer theory denote dependencies diagnostic accuracy distinguished node domain edge Equation example expansion order expected utility expert systems follicles given global knowledge map Heckerman hi,hj histiocytes Hodgkin's disease Holmes hs maps HYPERP hypotheses hypothesis-specific independence hypothesis-specific network hypothesis-specific similarity network inferential loss influence diagram instances intelligent decision system joint distribution knowledge base lottery lymph-node lymphoma MONONUCLEOSIS nondistinguished nodes nondistinguished variables normative expert systems o-local map observation ordinary similarity network pair partition representations Pathfinder III pathologist PERITONSILLAR ABSCESS probabilistic probability assessments relevance set represent Return inconsistent shown in Figure similarity graph similarity-network and partition SimNet sound STREP THROAT subset independence Theorem TOXIC APPEARANCE VIRAL PHARYNGITIS
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