Symbolic heuristic search for factored Markov decision processes
Zhengzhu Feng, Eric A. Hansen · NASA STI Repository (National Aeronautics and Space Administration) · 2002
We describe a planning algorithm that integrates two ap-proaches 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 demon-strates their usefulness for decision-theoretic planning.