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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorKosmopoulos, Dimitrios I.-
dc.contributor.authorChatzis, Sotirios P.-
dc.date.accessioned2016-01-18T12:18:01Z-
dc.date.available2016-01-18T12:18:01Z-
dc.date.issued2014-
dc.identifier.citation24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings, pp. 355-362en_US
dc.identifier.isbn978-3-319-11179-7 (online)-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/8205-
dc.description.abstractPartially Observable Markov Decision Processes (POMDPs) have been met with great success in planning domains where agents must balance actions that provide knowledge and actions that provide reward. Recently, nonparametric Bayesian methods have been success- fully applied to POMDPs to obviate the need of a priori knowledge of the size of the state space, allowing to assume that the number of visited states may grow as the agent explores its environment. These approaches rely on the assumption that the agent's environment remains stationary; however, in real-world scenarios the environment may change over time. In this work, we aim to address this inadequacy by introducing a dynamic nonparametric Bayesian POMDP model that both allows for automatic inference of the (distributional) representations of POMDP states, and for capturing non-stationarity in the modeled environments. Formulation of our method is based on imposition of a suitable dynamic hierarchical Dirichlet process (dHDP) prior over state transitions. We derive e cientalgorithms for model inference and action planning and evaluate it on several benchmark tasks.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.subjectMarkov decision processesen_US
dc.subjectBayesian methodsen_US
dc.titleA non-stationary infinite partially-observable markov decision processen_US
dc.typeConference Papersen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationHellenic Mediterranean Universityen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.reviewPeer Revieweden
dc.countryCyprusen_US
dc.countryGreeceen_US
dc.subject.fieldEngineering and Technologyen_US
dc.relation.conferenceInternational Conference on Artificial Neural Networksen_US
dc.identifier.doi10.1007/978-3-319-11179-7_45en_US
dc.dept.handle123456789/134en
cut.common.academicyear2014-2015en_US
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4956-4013-
crisitem.author.parentorgFaculty of Engineering and Technology-
Εμφανίζεται στις συλλογές:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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