Edmund H. Durfee
Artificial Intelligence Laboratory, University of Michigan
1101 Beal Avenue, Ann Arbor, MI 48109-2110
We provide a framework for the study of learning in certain types of multi-agent systems (MAS), that divides an agent's knowledge about others into different ``types''. We use concepts from computational learning theory to calculate the relative sample complexities of learning the different types of knowledge, given either a supervised or a reinforcement learning algorithm. These results apply only for the learning of a fixed target function, which would probably not exist if the other agents are also learning. We then show how a changing target function affects the learning behaviors of the agents, and how to determine the advantages of having lower sample complexity. Our results can be used by a designer of a learning agent in a MAS to determine which knowledge he should put into the agent and which knowledge should be learned by the agent.