Uncertainty in Artificial Intelligence
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FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large POMDPs
Zhongzhang Zhang, Xiaoping Chen
Abstract:
Planning in partially observable Markov decision processes (POMDPs) remains a challenging topic in the artificial intelligence community, in spite of recent impressive progress in approximation techniques. Previous research has indicated that online planning approaches are promising in handling large-scale POMDP domains efficiently as they make decisions "on demand" instead of proactively for the entire state space. We present a Factored Hybrid Heuristic Online Planning (FHHOP) algorithm for large POMDPs. FHHOP gets its power by combining a novel hybrid heuristic search strategy with a recently developed factored state representation. On several benchmark problems, FHHOP substantially outperformed state-of-the-art online heuristic search approaches in terms of both scalability and quality.
Keywords:
Pages: 934-943
PS Link:
PDF Link: /papers/12/p934-zhang.pdf
BibTex:
@INPROCEEDINGS{Zhang12,
AUTHOR = "Zhongzhang Zhang and Xiaoping Chen",
TITLE = "FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large POMDPs",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "934--943"
}


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