Symbolic heuristic search for factored Markov decision processes

Authors:
Zhengzhu Feng;Eric A. Hansen
Affiliations:
Computer Science Department, University of Massachusetts, Amherst, MA;Computer Science Department, Mississippi State University, Mississippi State, MS
Venue:
Eighteenth national conference on Artificial intelligence
Year:
2002

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Abstract

We describe a plnning 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.