Uncertainty in Artificial Intelligence
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Symbolic Generalization for On-line Planning
Zhengzhu Feng, Eric Hansen, Shlomo Zilberstein
Abstract:
Symbolic representations have been used successfully in off-line planning algorithms for Markov decision processes. We show that they can also improve the performance of on-line planners. In addition to reducing computation time, symbolic generalization can reduce the amount of costly real-world interactions required for convergence. We introduce Symbolic Real-Time Dynamic Programming (or sRTDP), an extension of RTDP. After each step of on-line interaction with an environment, sRTDP uses symbolic model-checking techniques to generalizes its experience by updating a group of states rather than a single state. We examine two heuristic approaches to dynamic grouping of states and show that they accelerate the planning process significantly in terms of both CPU time and the number of steps of interaction with the environment.
Keywords:
Pages: 209-216
PS Link:
PDF Link: /papers/03/p209-feng.pdf
BibTex:
@INPROCEEDINGS{Feng03,
AUTHOR = "Zhengzhu Feng and Eric Hansen and Shlomo Zilberstein",
TITLE = "Symbolic Generalization for On-line Planning",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "209--216"
}


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