Pascal Poupart
Department of Computer Science
University of British Columbia
Vancouver, BC V6T 1Z4
email: ppoupart@cs.toronto.edu
Craig Boutilier
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: cebly@cs.toronto.edu
Abstract
We consider the problem belief-state monitoring for the purposes of
implementing a policy for a partially-observable Markov decision
process (POMDP), specifically how one might approximate the belief
state. Other schemes for belief-state approximation (e.g., based on
minimizing a measure such as KL-divergence between the true and
estimated state) are not necessarily appropriate for POMDPs. Instead
we propose a framework for analyzing
To appear, UAI-2000
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