Artificial intelligence dialects of the Bayesian belief revision language

Several well-known belief languages in artificial intelligence are reviewed, and both previous work and new insights into their Bayesian interpretations are presented. In particular, the authors focus on three alternative belief-update models: the certainty factors calculus, Dempster-Shafer simple support functions, and the descriptive contrast/inertia model. Important 'dialects' of these languages are shown to be isomorphic to each other and to a special case of Bayesian inference. Parts of the analysis were carried out by other authors; their results were extended and consolidated using an analytic technique designed to study the kinship of belief languages in general.<
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