FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large POMDPs

Zhongzhang Zhang, Xiaoping Chen
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, PMLR R10:933-942, 2012.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR10-zhang12a, title = {{FHHOP}: A Factored Hybrid Heuristic Online Planning Algorithm for Large POMDPs}, author = {Zhang, Zhongzhang and Chen, Xiaoping}, booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence}, pages = {933--942}, year = {2012}, editor = {de Freitas, Nando and Murphy, Kevin}, volume = {R10}, series = {Proceedings of Machine Learning Research}, month = {14--18 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r10/main/assets/zhang12a/zhang12a.pdf}, url = {https://proceedings.mlr.press/r10/zhang12a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large POMDPs %A Zhongzhang Zhang %A Xiaoping Chen %B Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2012 %E Nando de Freitas %E Kevin Murphy %F pmlr-vR10-zhang12a %I PMLR %P 933--942 %U https://proceedings.mlr.press/r10/zhang12a.html %V R10 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Zhang, Z. & Chen, X.. (2012). FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large POMDPs. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R10:933-942 Available from https://proceedings.mlr.press/r10/zhang12a.html. Reissued by PMLR on 04 October 2026.

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