Hybrid negotiation for resource coordination in multiagent systems

In this paper, we present a coordination approach to resource allocation problem in multiagent systems. Agents adaptively coordinate resources among themselves to handle resource shortage crises resulted from events they encounter in dynamic, uncertain, real-time, and noisy environments. The coordination approach is implemented with a hybrid negotiation mechanism. The hybrid negotiation mechanism combines competitive and cooperative negotiations. In competitive negotiations, agents are self-interested and negotiate to maximize their individual performance; while in cooperative negotiations, agents are altruistic and negotiate to find a solution to help others. We define a hybrid negotiation model based on the Belief-Desire-Intention (BDI) architecture of agents, and implement the model with a specific negotiation protocol and strategy. To help agents negotiate better, we equip agents with profiling and learning capabilities. Agents profile others and learn the negotiation experience to make decisions on with whom to negotiate, and how to negotiate. We have implemented a multiagent system for coordinating the CPU resource allocation among agents based on the hybrid negotiation mechanism and conducted a series of experiments. The experimental results show that our coordination approach to resource allocation is able to reduce resource shortage crises, make the multiagent system adaptive to the variation of load, and provide efficient resource coordination among autonomous agents. The experimental results also show that the hybrid negotiation mechanism is stable for resource coordination in complex environments.