@InProceedings{   vidal:97a,
  author =	 {Jos\'{e} M. Vidal and Edmund H. Durfee},
  title =	 {Agents Learning about Agents: A Framework and
                  Analysis},
  booktitle =	 {Multiagent Learning Workshop},
  url =		 {http://jmvidal.cse.sc.edu/papers/lmas/},
  year =	 {1997},
  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. },
  googleid = 	 {WrPbNSW5P84J:scholar.google.com/},
  keywords = 	 {multiagent learning},
  cluster = 	 {14861800864814445402}
}