Stochastic Boolean Satisfiability
Journal of Automated Reasoning
CSP properties for quantified constraints: definitions and complexity
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Scenario-based stochastic constraint programming
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An optimal coarse-grained arc consistency algorithm
Artificial Intelligence
On the stochastic constraint satisfaction framework
Proceedings of the 2007 ACM symposium on Applied computing
Cost-Based Domain Filtering for Stochastic Constraint Programming
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Stochastic satisfiability modulo theories for non-linear arithmetic
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Synthesizing filtering algorithms for global chance-constraints
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Evolving parameterised policies for stochastic constraint programming
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Resolution for stochastic Boolean satisfiability
LPAR'10 Proceedings of the 17th international conference on Logic for programming, artificial intelligence, and reasoning
Preferences in AI: An overview
Artificial Intelligence
Generalized craig interpolation for stochastic boolean satisfiability problems
TACAS'11/ETAPS'11 Proceedings of the 17th international conference on Tools and algorithms for the construction and analysis of systems: part of the joint European conferences on theory and practice of software
Filtering algorithms for global chance constraints
Artificial Intelligence
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The Stochastic CSP (SCSP) is a framework recently introduced by Walsh to capture combinatorial decision problems that involve uncertainty and probabilities. The SCSP extends the classical CSP by including both decision variables, that an agent can set, and stochastic variables that follow a probability distribution and can model uncertain events beyond the agent's control. So far, two approaches to solving SCSPs have been proposed; backtracking-based procedures that extend standard methods from CSPs, and scenario-based methods that solve SCSPs by reducing them to a sequence of CSPs. In this paper we further investigate the former approach. We first identify and correct a flaw in the forward checking (FC) procedure proposed by Walsh. We also extend FC to better take advantage of probabilities and thus achieve stronger pruning. Then we define arc consistency for SCSPs and introduce an arc consistency algorithm that can handle constraints of any arity.