In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains.
In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains.
In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains.
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What is a partially observable Markov decision process?
Definition. A partially observable Markov decision process (POMDP) refers to a class of sequential decision-making problems under uncertainty. This class includes problems with partially observable states and uncertain action effects.
In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains.
Upper bounds on the sample complexity are proved showing that, even for infinitely large and arbitrarily complex POMDPs, the amount of data needed can be ...
In this paper, we describe the partially observable. Markov decision process (POMDP) approach to finding optimal or near-optimal control strategies for ...
Consider the problem of a robot navigating in a large office building. The robot can move from hallway intersection to intersection and can make local ...
Nov 1, 1995 · Abstract. In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially ...
PDF | In this paper, we describe the partially observable Markov decision process (pomdp) approach to finding optimal or near-optimal control strategies.
In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. We ...