Symbolic Heuristic Search for Factored Markov Decision Processes

Zhengzhu Feng, University of Massachusetts; Eric A. Hansen, Mississippi State University

We describe a planning algorithm that integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to avoid evaluating states individually. Forward search from a start state, guided by an admissible heuristic, is used to avoid evaluating all states. We combine these two approaches in a novel way that exploits symbolic model-checking techniques and demonstrates their usefulness for decision-theoretic planning.


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