Solving POMDPs using quadratically constrained linear programs

Authors:
Christopher Amato;Daniel S. Bernstein;Shlomo Zilberstein
Affiliations:
University of Massachusetts, Amherst, MA;University of Massachusetts, Amherst, MA;University of Massachusetts, Amherst, MA
Venue:
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Year:
2006

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Abstract

Since the early 1990's, Markov decision processes (MDPs) and their partially observable counterparts (POMDPs) have been widely used by the AI community for planning under uncertainty. POMDPs offer a rich language to describe situations involving uncertainty about the domain, stochastic actions, noisy observations, and a variety of possible objective functions. Even though an optimal solution may be concise, current exact algorithms that use dynamic programming often require an intractable amount of space. POMDP approximation algorithms can operate with a limited amount of memory, but as a consequence they provide very weak theoretical guarantees. In contrast, we describe a new approach that addresses the space requirement of POMDP algorithms while maintaining well-defined optimality guarantees.