Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes (1997)
| Venue: | In Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence |
| Citations: | 144 - 9 self |
BibTeX
@INPROCEEDINGS{Cassandra97incrementalpruning:,
author = {Anthony Cassandra and Michael L. Littman and Nevin L. Zhang},
title = {Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes},
booktitle = {In Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence},
year = {1997},
pages = {54--61},
publisher = {Morgan Kaufmann Publishers}
}
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Abstract
Most exact algorithms for general partially observable Markov decision processes (pomdps) use a form of dynamic programming in which a piecewise-linear and convex representation of one value function is transformed into another. We examine variations of the "incremental pruning" method for solving this problem and compare them to earlier algorithms from theoretical and empirical perspectives. We find that incremental pruning is presently the most efficient exact method for solving pomdps. 1 INTRODUCTION Partially observable Markov decision processes (pomdps) model decision theoretic planning problems in which an agent must make a sequence of decisions to maximize its utility given uncertainty in the effects of its actions and its current state (Cassandra, Kaelbling, & Littman 1994; White 1991). At any moment in time, the agent is in one of a finite set of possible states S and must choose one of a finite set of possible actions A. After taking action a 2 A from state s 2 S, the agent...
