Search on Asymmetric DCOPs by Strategic Agents

Asymmetric Distributed Constraint Optimization Problems (ADCOPs) are a useful model for representing real-life problems of distributed nature. Constraining agents in ADCOPs have different gains (or costs) for the constraints that involve them. All former ADCOP search algorithms assume cooperation among the agents and do not capture the possibility of strategic behavior by the searching agents. The present paper extends a recent approach that uses side payments among constraining agents in ADCOP local search, and proposes an improved such algorithm for strategic agents. Enabling search for strategic agents is especially suitable for asymmetric DCOPs, where the agents gain differently from the constraints and would naturally pursue personal gains.The proposed method uses a specially designed mechanism that enforces truthful behavior for agents placing bids of side payments during search. This in turn guarantees that the (strategic) agents' bids will form bids of maximal payoffs. The resulting search algorithm is an anytime algorithm that converges to stable solutions of higher social welfare that are local optima of the global social welfare, and computes the payments (contracts) that stabilize its outcome as a pure Nash equilibrium (PNE). The experimental evaluation shows that the payments charged by the mechanism in order to enforce truthful behavior are small compared to the total increase in the social welfare, and that the vast majority of the agents have improved personal gains when the algorithm terminates.