Proceedings of the 7th Conference of the Cognitive Science Society, University of California, Irvine, CA. pp. 329–334

This paper explores the paradigm that heuristics are discovered by consulting simplified models of the problem domain. After describing the features of typical heuristics on some popular problems, we demonstrate that these heuristics can be obtained by the process of deleting constraints from the original problem and solving the relaxed problem which ensues. We then outline a scheme for generating such heuristics mechanically, which involves systematic refinement and deletion of constraints from the original problem specification until a semidecomposable model is identified. The solution to the latter constitutes a heuristic for the former.

This paper presents generalizations of Bayes likelihood-ratio updating rule which facilitate an asynchronous propagation of the impacts of new beliefs and/or new evidence in hierarchically organized inference structures with multi-hypotheses variables. The computational scheme proposed specifies a set of belief parameters, communication messages and updating rules which guarantee that the diffusion of updated beliefs is accomplished in a single pass and complies with the tenets of Bayes calculus.Proc AAAI