Agents Learning about Agents: A Framework and Analysis
José M.
Vidal and
Edmund H. Durfee
Artificial Intelligence
Laboratory,
University of Michigan
1101 Beal Avenue, Ann Arbor, MI 48109-2110
jmvidal@umich.edu
Abstract:
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.
Postscript version.
Jose M. Vidal
jmvidal@umich.edu
Thu Apr 24 13:32:36 EDT 1997