Abstract: Agents increase capabilities and receive more repayment via coalition in multi-agent system. This paper focuses on the improvement of coalition model and coalition formation, and proposes a new coalition model CLAR(coalition model based on learning agent and role), which is based on ARG (agent, role, group) meta model and learning mechanism. It also proposes a two phrase coalition formation mechanism in CLAR model that adopts contract net as its protocol. Finally, the experimental results verify the effect of the role and learning mechanism in predator game, and the effect of two-phrase coalition formation in decreasing and controlling the communication cost.

Key words: multi-agent system, coalition, predator game, ARG meta model, contract net

摘要： 多Agent系统中，Agent间通过形成联盟达到提高任务求解能力，获取更多收益的目的。主要关注联盟模型的改进和联盟形成阶段的改进，基于ARG（agent，role，group）元模型和学习机制提出了一种采用角色和学习机制的新联盟模型CLAR（coalition model based on learning agent and role）；在采用合同网协议的 CLAR联盟模型中提出了两阶段联盟形成机制；通过捕食者问题实验验证了角色和学习机制的作用，以及两阶段联盟形成机制在减少通讯代价上的作用。

关键词: 多Agent系统, 联盟, 捕食者问题, ARG元模型, 合同网