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Title: Structured parameter elicitation
Authors: Ko, L.L.
Hsu, D. 
Lee, W.S. 
Ong, S.C.W. 
Issue Date: 2010
Source: Ko, L.L.,Hsu, D.,Lee, W.S.,Ong, S.C.W. (2010). Structured parameter elicitation. Proceedings of the National Conference on Artificial Intelligence 2 : 1102-1107. ScholarBank@NUS Repository.
Abstract: The behavior of a complex system often depends on parameters whose values are unknown in advance. To operate effectively, an autonomous agent must actively gather information on the parameter values while progressing towards its goal. We call this problem parameter elicitation. Partially observable Markov decision processes (POMDPs) provide a principled framework for such uncertainty planning tasks, but they suffer from high computational complexity. However, POMDPs for parameter elicitation often possess special structural properties, specifically, factorization and symmetry. This work identifies these properties and exploits them for efficient solution through a factored belief representation. The experimental results show that our new POMDP solvers outperform SARSOP and MOMDP, two of the fastest general-purpose POMDP solvers available, and can handle significantly larger problems. Copyright © 2010, Association for the Advancement of Artificial Intelligence ( All rights reserved.
Source Title: Proceedings of the National Conference on Artificial Intelligence
ISBN: 9781577354659
Appears in Collections:Staff Publications

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