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dc.contributor.authorArib, Souhila
hal.structure.identifier
dc.contributor.authorAknine, Souhila
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorCazenave, Tristan
HAL ID: 743184
dc.date.accessioned2020-06-08T14:17:10Z
dc.date.available2020-06-08T14:17:10Z
dc.date.issued2015
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20848
dc.descriptionLecture Notes in Computer Science book series (LNCS, volume 9426)en
dc.language.isoenen
dc.subjectMulti-agent systemsen
dc.subjectCoalition formationen
dc.subjectCoordinationen
dc.subjectNegotiationen
dc.subject.ddc006.3en
dc.titleNested Monte-Carlo Search for Multi-Agent Coalitions Mechanism With Constraintsen
dc.typeCommunication / Conférence
dc.description.abstractenThis paper develops and evaluates a coalition mechanism that enables agents to participate in concurrent tasks achievement in competitive situations in which agents have several constraints. Here we focus on situations in which the agents are self-interested and have not a priori knowledge about the preferences of their opponents, and they have to cooperate in order to reach their goals. All the agents have their specific constraints and this information is private. The agents negotiate for coalition formation (CF) over these constraints, that may be relaxed during negotiations. They start by exchanging their constraints and making proposals, which represent their acceptable solutions, until either an agreement is reached, or the negotiation terminates. We explore two techniques that ease the search of suitable coalitions: we use a constraint-based model and a heuristic search method. We describe a procedure that transforms these constraints into a structured graph on which the agents rely during their negotiations to generate a graph of feasible coalitions. This graph is therefore explored by a Nested Monte-Carlo search algorithm to generate the best coalitions and to minimize the negotiation time.en
dc.identifier.citationpages80-88en
dc.relation.ispartoftitleMulti-disciplinary Trends in Artificial Intelligenceen
dc.relation.ispartofeditorBikaki, Antonis
dc.relation.ispartofeditorZheng, Xianghan
dc.relation.ispartofpublnameSpringeren
dc.relation.ispartofurl10.1007/978-3-319-26181-2en
dc.subject.ddclabelIntelligence artificielleen
dc.relation.ispartofisbn978-3-319-26181-2en
dc.relation.conftitle9th International Workshop on Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2015)en
dc.relation.confdate2015-11
dc.relation.confcityFuzhouen
dc.relation.confcountryChinaen
dc.relation.forthcomingnonen
dc.identifier.doi10.1007/978-3-319-26181-2_8en
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2020-06-08T14:12:47Z
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