On the stochastic constraint satisfaction framework

Authors:
Lucas Bordeaux;Horst Samulowitz
Affiliations:
Microsoft Research, Cambridge, UK;University of Toronto, Toronto, Canada
Venue:
Proceedings of the 2007 ACM symposium on Applied computing
Year:
2007

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Abstract

Stochastic constraint satisfaction is a framework that allows to make decisions taking into account possible futures. We study two challenging aspects of this framework: (1) variables in stochastic CSP are ordered sequentially, which is adequate for the representation of a number of problems, but is not a natural choice for the modeling of problems in which the future can follow different branches (2) the framework was designed to allow multi-objective decision-making, yet this issue has been treated only superficially in the literature. We bring a number of clarifications to these two aspects. In particular, we show how minor modifications allow the framework to deal with non-sequential forms, we identify a number of technicalities related to the use of the sequential ordering of variables and of the use of multiple objectives, and in addition we propose the first search algorithm that solves multi-objective stochastic problems in polynomial space.