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by Rina Dechter
in: International Conference on Artificial Intelligence Planning Systems (AIPS-2000
ftp://ftp.ics.uci.edu/pub/CSP-repository/papers/r82.ps
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Abstract:
The paper takes a fresh look at algorithms for maximizing expected utility over a set of policies, that is, a set of possible ways of reacting to observations about an uncertain state of the world. Using the bucketelimination framework, we characterize the complexity of this optimization task by graph-based parameters, and devise an improved variant of existing algorithms. The improvement is shown to yield a dramatic gain in complexity when the probabilistic subgraph (of the influence diagram) is sparse, regardless of the complexity introduced by its utility subgraph.
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