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Symbolic Heuristic Search for Factored Markov Decision ProcessesWe 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.
Document ID
20030034769
Acquisition Source
Headquarters
Document Type
Other
Authors
Morris, Robert (NASA Ames Research Center Moffett Field, CA, United States)
Feng, Zheng-Zhu (Massachusetts Univ. Amherst, MA, United States)
Hansen, Eric A. (Mississippi State Univ. Mississippi State, MS, United States)
Date Acquired
September 7, 2013
Publication Date
January 1, 2003
Subject Category
Statistics And Probability
Funding Number(s)
CONTRACT_GRANT: NAG2-1463
CONTRACT_GRANT: NSF IIS-99-84952
CONTRACT_GRANT: NAG2-1394
Distribution Limits
Public
Copyright
Public Use Permitted.

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