Uncertainty in Artificial Intelligence
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A Constraint Satisfaction Approach to Decision under Uncertainty
Helene Fargier, Jerome Lang, Roger Martin-Clouaire, Thomas Schiex
The Constraint Satisfaction Problem (CSP) framework offers a simple and sound basis for representing and solving simple decision problems, without uncertainty. This paper is devoted to an extension of the CSP framework enabling us to deal with some decisions problems under uncertainty. This extension relies on a differentiation between the agent-controllable decision variables and the uncontrollable parameters whose values depend on the occurrence of uncertain events. The uncertainty on the values of the parameters is assumed to be given under the form of a probability distribution. Two algorithms are given, for computing respectively decisions solving the problem with a maximal probability, and conditional decisions mapping the largest possible amount of possible cases to actual decisions.
Keywords: Constraint satisfaction, decision under uncertainty, conditional decisions, anytime
Pages: 167-174
PS Link: ftp://ftp.irit.fr/private/CSFDU.ps
PDF Link: /papers/95/p167-fargier.pdf
AUTHOR = "Helene Fargier and Jerome Lang and Roger Martin-Clouaire and Thomas Schiex",
TITLE = "A Constraint Satisfaction Approach to Decision under Uncertainty",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "167--174"

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