An Integrated Multilevel Learning Approach to Multiagent Coalition Formation

In this paper we describe an integrated multilevel learning approach to multiagent coalition formation in a real-time environment. In our domain, agents negotiate to form teams to solve joint problems. The agent that initiates a coalition shoulders the responsibility of overseeing and managing the formation process. A coalition formation process consists of two stages. During the initialization stage, the initiating agent identifies the candidates of its coalition, i.e., known neighbors that could help. The initiating agent negotiates with these candidates during the finalization stage to determine the neighbors that are willing to help. Since our domain is dynamic, noisy, and time-constrained, the coalitions are not optimal. However, our approach employs learning mechanisms at several levels to improve the quality of the coalition formation process. At a tactical level, we use reinforcement learning to identify viable candidates based on their potential utility to the coalition, and ...

• Type: Conference
• Year: 2003
• Where: IJCAI
• Authors: Leen-Kiat Soh, Xin Li
• Keywords: Coalition Formation, Coalition Formation Process, IJCAI 2003, IJCAI 2007, Multiagent Coalition Formation