Agent-behavior strategy in serial multi-task coalition formation
多任务联盟形成中的Agent行为策略研究

Agent-coalition is an important approach to agent-coordination and cooperation, in which the coalition formation is a key topic. Existing researches are restricted in single-task environments, and the results are not applied to multi-task environments. In this paper, a new agent behavior strategy in serial multi-task coalition formation for problemsolving is presented. The conclusion shows that the agent-task selection is a Markov Decision Process. The Q-learning is used to optimize the behavior strategy for a single agent, and the cooperative multi-agent reinforcement learning improves the learning rate. Experiments prove that the strategy can effectively and serially form coalitions for multi-task.