AbstractDistributed multi-agent agreement problems (MAPs) are central to many multi-agent systems. However, to date, the issues associated with encounters between self-interested and privacy-preserving agents have received limited attention. Given this, we develop the first distributed negotiation mechanism that enables self-interested agents to reach a socially desirable agreement with limited information leakage. The agents’ optimal negotiation strategies in this mechanism are investigated. Specifically, we propose a reinforcement learning-based approach to train agents to learn their optimal strategies in the proposed mechanism. Also, a heuristic algorithm is designed to find close-to-optimal negotiation strategies with reduced computational costs. We demonstrate the effectiveness and strength of our proposed mechanism through both game theoretical and numerical analysis. We prove theoretically that the proposed mechanism is budget balanced and motivates the agents to participate and follow the rules faithfully. The experimental results confirm that the proposed mechanism significantly outperforms the current state of the art, by increasing the social-welfare and decreasing the privacy leakage.
Scalable Approach to Dynamic SLA Negotiation Mechanism in Protected Shared Mesh Optical Networks
