A computational model for causal and diagnostic reasoning in inference systems

International Joint Conference on Artificial IntelligencePublished 8 August 1983

Jin Hee Kim, Judea Pearl

Citations236

TL;DR

A representation of evidential relationships which permits updating of belief in two simultaneous modes: causal and diagnostic is introduced, which extends the hierarchical tree representation by allowing multiple causes to a given manifestation.

Abstract

This paper introduces a representation of evidential relationships which permits updating of belief in two simultaneous modes: causal (i. e. top-down) and diagnostic (i.e. bottom-up). It extends the hierarchical tree representation by allowing multiple causes to a given manifestation. We develop an updating scheme that obeys the axioms of probability, is computationally efficient, and is compatible with experts reasoning. The belief parameters of each variable are defined and updated by those of its neighbors in such a way that the impact of each new evidence propagates and settles through the network in a single pass.

Keywords

Computer Science

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