Cooperation of decision-theoretic agents in the context of multi-agent systems

Multi-agent based solutions to problems, whether they are software agents or physical robots, are attractive because they are robust and scalable. Fundamental aspects of designing multi-agent systems involve modeling the intelligence of the agents and modeling their interactions. The intelligences of agents modeled here are encoded in Bayesian representations of their world. The agents interact only by observing others and moving in such a way so as to probabilistically maximize their internalized goal or utility. Using this multi-agent framework, a three agent herding is explored.