Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2005

Abstract

Agents or agent teams deployed to assist humans often face the challenges of monitoring the state of key processes in their environment (including the state of their human users themselves) and making periodic decisions based on such monitoring. POMDPs appear well suited to enable agents to address these challenges, given the uncertain environment and cost of actions, but optimal policy generation for POMDPs is computationally expensive. This paper introduces three key techniques to speedup POMDP policy generation that exploit the notion of progress or dynamics in personal assistant domains. Policy computation is restricted to the belief space polytope that remains reachable given the progress structure of a domain. We introduce new algorithms; particularly one based on applying Lagrangian methods to compute a bounded belief space support in polynomial time. Our techniques are complementary to many existing exact and approximate POMDP policy generation algorithms. Indeed, we illustrate this by enhancing two of the fastest existing algorithms for exact POMDP policy generation. The order of magnitude speedups demonstrate the utility of our techniques in facilitating the deployment of POMDPs within agents assisting human users.

Keywords

meeting rescheduling, task allocation, partially observable markov decision process (POMDP)

Discipline

Artificial Intelligence and Robotics | Business | Operations Research, Systems Engineering and Industrial Engineering

Publication

AAMAS '05: The fourth International Joint Conference on Autonomous Agents and Multi Agent Systems, Utrecht University, the Netherlands, July 25-29, 2005

First Page

978

Last Page

985

ISBN

9781595930934

Identifier

10.1145/1082473.1082621

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

http://dx.doi.org/10.1145/1082473.1082621

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