Part of the knowledge and set of beliefs of a cognitive agent are its mental models of the world and of other agents. A mental model reflects the cultural context and past experiences of an adaptive agent. In interacting with other agents, we claim that a causal mental model, i.e. a cognitive map, which might be different for each agent, adapts and changes causing coalitions to emerge. Possible actions are reflected through the respective cognitive maps of the agents to determine whether a favorable steady state will emerge from a coalition. The problems addressed are which coalitions will be more likely to form and how to adapt the cognitive maps of the agents in order to reduce conflicts. The claim is that a group mental model can emerge from individual mental models through the learning and adaptation of cognitive maps. An algorithm based on Particle Swarm Optimization (PSO) for evolving cognitive maps for coalition formation is introduced and experiments on randomly generated cognitive maps are presented. We conclude with possible uses of this adaptive cognitive modeling approach to understand other cultures, predict coalitions/chaos and shifts of allegiance, and induce group formation through avatars in a virtual world.
Subjects: 7.1 Multi-Agent Systems; 12.1 Reinforcement Learning
Submitted: Jun 20, 2008