LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Symbolic heuristic search for factored Markov decision processes
Eighteenth national conference on Artificial intelligence
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
Planning under continuous time and resource uncertainty: a challenge for AI
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Markov Decision Processes (MDPs) and contingency planning (CP) are two widely used approaches to planning under uncertainty. MDPs are attractive because the model is extremely general and because many algorithms exist for deriving optimal plans. In contrast, CP is normally performed using heuristic techniques that do not guarantee optimality, but the resulting plans are more compact and more understandable. The inability to present MDP policies in a clear, intuitive way has limited their applicability in some important domains. We introduce an anytime algorithm for deriving contingency plans that combines the advantages of the two approaches.