Uncertainty in Artificial Intelligence
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The Compilation of Decision Models
David Heckerman, John Breese, Eric Horvitz
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.
Pages: 162-173
PS Link:
PDF Link: /papers/89/p162-heckerman.pdf
AUTHOR = "David Heckerman and John Breese and Eric Horvitz",
TITLE = "The Compilation of Decision Models",
BOOKTITLE = "Proceedings of the Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-89)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1989",
PAGES = "162--173"

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