An optimal rewiring strategy for cooperative multiagent social learning
Multiagent coordination is a key problem in cooperative multiagent systems (MASs). It has been widely studied in both fixed-agent repeated interaction setting and static social learning framework. However, two aspects of dynamics in real-world MASs are currently neglected. First, the network topologies can change during the course of interaction dynamically. Second, the interaction utilities can be different among each pair of agents and usually unknown before interaction. Both issues mentioned above increase the difficulty of coordination. In this paper, we consider the multiagent social learning in a dynamic environment in which agents can alter their connections and interact with randomly chosen neighbors with unknown utilities beforehand. We propose an optimal rewiring strategy to select most beneficial peers to maximize the accumulated payoffs in long-run interactions. We empirically demonstrate the effects of our approach in a variety of large-scale MASs.
|Keywords||Learning agent-to-agent interactions (negotiation, trust, coordination), Multiagent learning, Social simulation|
|Project||Representing Users in a Negotiation (RUN): An Autonomous Negotiator Under Preference Uncertainty|
|Conference||International Conference on Autonomous Agents and Multi-Agent Systems|
|Grant||This work was funded by the The Netherlands Organisation for Scientific Research (NWO); grant id nwo/639.021.751 - Representing Users in a Negotiation (RUN): An Autonomous Negotiator Under Preference Uncertainty|
Tang, H, Hao, J, Wang, L, Wang, Z, & Baarslag, T. (2019). An optimal rewiring strategy for cooperative multiagent social learning. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (pp. 2209–2211).