Nested Monte-Carlo Search for Multi-Agent Coalitions Mechanism With Constraints
Arib, Souhila; Aknine, Souhila; Cazenave, Tristan (2015), Nested Monte-Carlo Search for Multi-Agent Coalitions Mechanism With Constraints, in Bikaki, Antonis; Zheng, Xianghan, Multi-disciplinary Trends in Artificial Intelligence, Springer, p. 80-88. 10.1007/978-3-319-26181-2_8
TypeCommunication / Conférence
Conference title9th International Workshop on Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2015)
Book titleMulti-disciplinary Trends in Artificial Intelligence
Book authorBikaki, Antonis; Zheng, Xianghan
MetadataShow full item record
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)This 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.
Subjects / KeywordsMulti-agent systems; Coalition formation; Coordination; Negotiation
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