Journal of Computer and System Sciences
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Stochastic Boolean Satisfiability
Journal of Automated Reasoning
Computing Factored Value Functions for Policies in Structured MDPs
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Policy Iteration for Factored MDPs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Contingency Selection in Plan Generation
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Piecewise linear value function approximation for factored MDPs
Eighteenth national conference on Artificial intelligence
Contingent planning under uncertainty via stochastic satisfiability
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Max-norm projections for factored MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Anytime synthetic projection: maximizing the probability of goal satisfaction
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Approximate planning for factored POMDPs using belief state simplification
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Deliberation scheduling using GSMDPs in stochastic asynchronous domains
International Journal of Approximate Reasoning
Evolving parameterised policies for stochastic constraint programming
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
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We describe appssat, an anytime probabilistic contingent planner based on zander, a probabilistic contingent planner that operates by converting the planning problem to a stochastic satisfiability (Ssat) problem and solving that problem instead [S.M. Majercik, M.L. Littman, Contingent planning under uncertainty via stochastic satisfiability, Artificial Intelligence 147 (2003) 119-162]. The values of some of the variables in an Ssat instance are probabilistically determined; appssat considers the most likely instantiations of these variables (the most probable situations facing the agent) and attempts to construct an approximation of the optimal plan that succeeds under those circumstances, improving that plan as time permits. Given more time, less likely instantiations/situations are considered and the plan is revised as necessary. In some cases, a plan constructed to address a relatively low percentage of possible situations will succeed for situations not explicitly considered as well, and may return an optimal or near-optimal plan. We describe experimental results showing that appssat can find suboptimal plans in cases in which zander is unable to find the optimal (or any) plan. Although the test problems are small, the anytime quality of appssat means that it has the potential to efficiently derive suboptimal plans in larger, time-critical domains in which zander might not have sufficient time to calculate any plan. We also suggest further work needed to bring appssat closer to attacking real-world problems.