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

Search

Browse

My Space

Overlapping coalition formation via probabilistic topic modeling

Mamakos Michail, Chalkiadakis Georgios

Simple record


URIhttp://purl.tuc.gr/dl/dias/5D424E06-FF5A-4384-B4DF-969BF8EA1952-
Identifierhttps://dl.acm.org/citation.cfm?id=3238054-
Languageen-
Extent3 pagesen
TitleOverlapping coalition formation via probabilistic topic modelingen
CreatorMamakos Michailen
CreatorΜαμακος Μιχαηλel
CreatorChalkiadakis Georgiosen
CreatorΧαλκιαδακης Γεωργιοςel
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)en
Content SummaryResearch in cooperative games often assumes that agents know the coalitional values with certainty, and that they can belong to one coalition only. By contrast, this work assumes that the value of a coalition is based on an underlying collaboration structure emerging due to existing but unknown relations among the agents; and that agents can form overlapping coalitions. Specifically, we first propose Relational Rules, a novel representation scheme for cooperative games with overlapping coalitions, which encodes the aforementioned relations, and which extends the well-known MC-nets representation to this setting. We then present a novel decisionmaking method for decentralized overlapping coalition formation, which exploits probabilistic topic modeling-and, in particular, online Latent Dirichlet Allocation. By interpreting formed coalitions as documents, agents can effectively learn topics that correspond to profitable collaboration structures.en
Type of ItemΣύντομη Δημοσίευση σε Συνέδριοel
Type of ItemConference Short Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2019-10-11-
Date of Publication2018-
SubjectGame theoryen
SubjectMulti agent systemsen
SubjectStatisticsen
Bibliographic CitationM. Mamakos and G. Chalkiadakis, "Overlapping coalition formation via probabilistic topic modeling," in 17th International Conference on Autonomous Agents and Multiagent Systems, 2018., pp. 2010-2012.en

Services

Statistics