A Kernel-Oriented Model for Coalition-Formation in General Environments: Implementation and Results

No. 1: Agents, AI in Art and Entertainment, Knowledge Representation, and Learning

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Negotiation & Coalition

In this paper we present a model for coalition formation and payoff distribution in general environments. We focus on a reduced complexity kernel-oriented coalition formation model, and provide a detailed algorithm for the activity of the single rational agent. The model is partitioned into a social level and a strategic level, to distinguish between regulations that must be agreed upon and are forced by agent-designers, and strategies by which each agent acts at will. In addition, we present an implementation of the model and simulation results. From these we conclude that implementing the model for coalition formation among agents increases the benefits of the agents with reasonable time consumption. It also shows that more coalition formations yield more benefits to the agents.