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Belief selection in point-based planning algorithms for POMDPs

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dc.contributor.author Azar, Danielle
dc.contributor.author Izadi, Masoumeh T.
dc.contributor.author Precup, Doina
dc.date.accessioned 2017-03-16T07:04:49Z
dc.date.available 2017-03-16T07:04:49Z
dc.identifier.isbn 978-3-540-34630-2 en_US
dc.identifier.uri http://hdl.handle.net/10725/5374
dc.description.abstract Current point-based planning algorithms for solving partially observable Markov decision processes (POMDPs) have demonstrated that a good approximation of the value function can be derived by interpolation from the values of a specially selected set of points. The performance of these algorithms can be improved by eliminating unnecessary backups or concentrating on more important points in the belief simplex. We study three methods designed to improve point-based value iteration algorithms. The first two methods are based on reachability analysis on the POMDP belief space. This approach relies on prioritizing the beliefs based on how they are reached from the given initial belief state. The third approach is motivated by the observation that beliefs which are the most overestimated or underestimated have greater influence on the precision of value function than other beliefs. We present an empirical evaluation illustrating how the performance of point-based value iteration (Pineau et al., 2003) varies with these approaches. en_US
dc.language.iso en en_US
dc.publisher Springer en_US
dc.title Belief selection in point-based planning algorithms for POMDPs en_US
dc.type Conference Paper / Proceeding en_US
dc.author.school SAS en_US
dc.author.idnumber 198833240 en_US
dc.author.department Computer Science and Mathematics en_US
dc.description.embargo N/A en_US
dc.identifier.doi http://dx.doi.org/10.1007/11766247_33 en_US
dc.identifier.ctation Izadi, M. T., Precup, D., & Azar, D. (2006, June). Belief selection in point-based planning algorithms for POMDPs. In Conference of the Canadian Society for Computational Studies of Intelligence (pp. 383-394). Springer Berlin Heidelberg. en_US
dc.author.email danielle.azar@lau.edu.lb en_US
dc.conference.date 383-394 en_US
dc.conference.title Conference of the Canadian Society for Computational Studies of Intelligence en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url https://link.springer.com/chapter/10.1007/11766247_33 en_US
dc.publication.date 2006 en_US
dc.author.affiliation Lebanese American University en_US
dc.title.volume Advances in Artificial Intelligence en_US


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