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Robust planning with (L)RTDP

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dc.contributor.authorBuffet, Olivieren
dc.contributor.authorAberdeen, Douglasen
dc.date.accessioned2025-06-05T21:34:42Z
dc.date.available2025-06-05T21:34:42Z
dc.date.issued2005en
dc.description.abstractStochastic Shortest Path problems (SSPs), a subclass of Markov Decision Problems (MDPs), can be efficiently dealt with using Real-Time Dynamic Programming (RTDP). Yet, MDP models are often uncertain (obtained through statistics or guessing). The usual approach is robust planning: searching for the best policy under the worst model. This paper shows how RTDP can be made robust in the common case where transition probabilities are known to lie in a given interval.en
dc.description.statusPeer-revieweden
dc.format.extent6en
dc.identifier.issn1045-0823en
dc.identifier.scopus78650598019en
dc.identifier.urihttp://www.scopus.com/inward/record.url?scp=78650598019&partnerID=8YFLogxKen
dc.identifier.urihttps://hdl.handle.net/1885/733757830
dc.language.isoenen
dc.relation.ispartofseries19th International Joint Conference on Artificial Intelligence, IJCAI 2005en
dc.sourceIJCAI International Joint Conference on Artificial Intelligenceen
dc.titleRobust planning with (L)RTDPen
dc.typeConference paperen
dspace.entity.typePublicationen
local.bibliographicCitation.lastpage1219en
local.bibliographicCitation.startpage1214en
local.contributor.affiliationBuffet, Olivier; School of Computing, ANU College of Systems and Society, The Australian National Universityen
local.contributor.affiliationAberdeen, Douglas; School of Computing, ANU College of Systems and Society, The Australian National Universityen
local.identifier.ariespublicationMigratedxPub13789en
local.identifier.pure464dccc7-266c-4508-90e6-8b32d777fc4cen
local.identifier.urlhttps://www.scopus.com/pages/publications/78650598019en
local.type.statusPublisheden

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