Distributed Revision of Belief Commitment in Multi-Hypothesis Interpretations

TL;DR

This paper introduces a distributed approach for revising belief commitments in belief networks, enabling efficient identification of the most satisfactory explanations in both singly and multiply-connected networks, with applications in medical diagnosis.

Contribution

It develops a coherent model for non-monotonic reasoning and presents distributed algorithms for belief revision, improving efficiency in complex belief network interpretations.

Findings

Linear-time algorithm for singly connected networks

Tractable interpretation in sparse multiply-connected networks

Application demonstrated in medical diagnosis

Abstract

This paper extends the applications of belief-networks to include the revision of belief commitments, i.e., the categorical acceptance of a subset of hypotheses which, together, constitute the most satisfactory explanation of the evidence at hand. A coherent model of non-monotonic reasoning is established and distributed algorithms for belief revision are presented. We show that, in singly connected networks, the most satisfactory explanation can be found in linear time by a message-passing algorithm similar to the one used in belief updating. In multiply-connected networks, the problem may be exponentially hard but, if the network is sparse, topological considerations can be used to render the interpretation task tractable. In general, finding the most probable combination of hypotheses is no more complex than computing the degree of belief for any individual hypothesis. Applications to medical diagnosis are illustrated.