Authors:
Souhila Arib
1
and
Samir Aknine
2
Affiliations:
1
Laboratoire Quartz, EISTI, France
;
2
Laboratoire LIRIS, Université Claude Bernard, Lyon 1, France
Keyword(s):
Multi-agent Systems, Coalition Formation, Coordination, Negotiation.
Abstract:
In multiple realistic scenarios, limited agent capabilities may negatively affect task performance. To overcome this, multi-agent cooperation may be required. Many studies have focused on cooperative task performance. To facilitate such cooperation, we develop and evaluate a coalition mechanism that enables agents to participate in concurrent tasks achievement in competitive situations, in which agents have several constraints. We consider a set of self-interested bounded-rational agents, each of which has a set of tasks, that leads an agent to achieving its goal. The agents have not a priori knowledge about the preferences of their opponents. All the agents have their specific constraints and this information is private. In this paper, we do not deal with the negotiation protocol but just introduce a new coalition formation mechanism (CF M ) that imposes minimal sharing of private information to ease negotiations. Specifically, we only require that agents share preferences over thei
r constraints. The agents negotiate for coalition formation 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.
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