Distributed Revision of Belief Commitment in Multi-Hypothesis Interpretations
This work addresses the challenge of efficient belief revision in AI systems, particularly for applications like medical diagnosis, though it appears incremental as it extends existing belief-network methods.
The paper tackles the problem of revising belief commitments in multi-hypothesis interpretations using belief networks, establishing a coherent non-monotonic reasoning model and presenting distributed algorithms. It shows that the most satisfactory explanation can be found in linear time in singly connected networks, while in sparse multiply-connected networks, topological considerations make the task tractable, with complexity no worse than computing individual belief degrees.
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.