Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Influence of state-variable constraints on partially observable Monte Carlo planning

Castellini Alberto, Chalkiadakis Georgios, Farinelli Alessandro

Simple record


URIhttp://purl.tuc.gr/dl/dias/CC802758-9A21-4D99-A2B7-E8EF862C8D47-
Identifierhttps://doi.org/10.24963/ijcai.2019/769-
Identifierhttps://www.ijcai.org/Proceedings/2019/769-
Languageen-
Extent7 pagesen
TitleInfluence of state-variable constraints on partially observable Monte Carlo planningen
CreatorCastellini Albertoen
CreatorChalkiadakis Georgiosen
CreatorΧαλκιαδακης Γεωργιοςel
CreatorFarinelli Alessandroen
PublisherInternational Joint Conferences on Artificial Intelligenceen
Content SummaryOnline planning methods for partially observable Markov decision processes (POMDPs) have recently gained much interest. In this paper, we propose the introduction of prior knowledge in the form of (probabilistic) relationships among discrete state-variables, for online planning based on the well-known POMCP algorithm. In particular, we propose the use of hard constraint networks and probabilistic Markov random fields to formalize state-variable constraints and we extend the POMCP algorithm to take advantage of these constraints. Results on a case study based on Rocksample show that the usage of this knowledge provides significant improvements to the performance of the algorithm. The extent of this improvement depends on the amount of knowledge encoded in the constraints and reaches the 50% of the average discounted return in the most favorable cases that we analyzed.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2020-10-29-
Date of Publication2019-
SubjectMarkov decision processesen
SubjectPOMCPen
SubjectOnline planning methodsen
Bibliographic CitationA. Castellini, G. Chalkiadakis and A. Farinelli, "Influence of state-variable constraints on partially observable Monte Carlo planning," in 28th International Joint Conference on Artificial Intelligence, 2019, pp. 5540-5546. doi: 10.24963/ijcai.2019/769en

Available Files

Services

Statistics