Please use this identifier to cite or link to this item:
|Title:||Structured parameter elicitation|
|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 (www.aaai.org). All rights reserved.|
|Source Title:||Proceedings of the National Conference on Artificial Intelligence|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 21, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.