Journal of Computer and System Sciences
Stochastic Boolean Satisfiability
Journal of Automated Reasoning
Contingency Selection in Plan Generation
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Contingent planning under uncertainty via stochastic satisfiability
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
A model approximation scheme for planning in partially observable stochastic domains
Journal of Artificial Intelligence Research
Computing factored value functions for policies in structured MDPs
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Anytime synthetic projection: maximizing the probability of goal satisfaction
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Policy iteration for factored MDPs
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Contingent planning under uncertainty via stochastic satisfiability
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
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We describe APPSSAT, an approximate 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 [1]. 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. This means that APPSSAT can sometimes find optimal plans faster than ZANDER. And the anytime quality of APPSSAT means that suboptimal plans could be efficiently derived in larger time-critical domains in which ZANDER might not have sufficient time to calculate the optimal plan. We describe some preliminary experimental results and suggest further work needed to bring APPSSAT closer to attacking real-world problems.