David Heckerman, Jack Breese, Eric Horvitz
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We introduce and analyze the problem of the compilation of decision models from a decision-theoretic perspective. The techniques described allow us to evaluate various configurations of compiled knowledge given the nature of evidential relationships in a domain, the utilities associated with alternative actions, the costs of run-time delays, and the costs of memory. We describe procedures for selecting a subset of the total observations available to be incorporated into a compiled situation-action mapping, in the context of a binary decision with conditional independence of evidence. The methods allow us to incrementally select the best pieces of evidence to add to the set of compiled knowledge in an engineering setting. After presenting several approaches to compilation, we exercise one of the methods to provide insight into the relationship between the distribution over weights of evidence and the preferred degree of compilation.
Keywords: Situation--action rules, reactive planning, reactive systems, probabilistic reasoning, Bayesian networks, compilation of action.
D. Heckerman, J.S. Breese, E. Horvitz, The Compilation of Decision Models, Proceedings of the Conference on Uncertainty in Artificial Intelligence, Association for Uncertainty in Artificial Intelligence, July 1989, pages 162-173.