@InProceedings{vu06a,
  author =	 {Thuc Vu and Rob Powers and Yoav Shoham},
  title =	 {Learning Against Multiple Opponents},
  booktitle =	 {Proceedings of the Fifth International Joint
                  Conference on Autonomous Agents and Multiagent
                  Systems},
  crossref =	 {aamas06},
  pages =	 {752--760},
  year =	 2006,
  abstract =	 {We address the problem of learning in repeated
                  n-player (as opposed to 2-player) general-sum games,
                  paying particular attention to the rarely addressed
                  situation in which there are a mixture of agents of
                  different types. We propose new criteria requiring
                  that the agents employing a particular learning
                  algorithm work together to achieve a joint
                  best-response against a target class of opponents,
                  while guaranteeing they each achieve at least their
                  individual security-level payoff against any
                  possible set of opponents. We then provide
                  algorithms that provably meet these criteria for two
                  target classes: stationary strategies and adaptive
                  strategies with a bounded memory. We also
                  demonstrate that the algorithm for stationary
                  strategies outperforms existing algorithms in tests
                  spanning a wide variety of repeated games with more
                  than two players.},
  url = 	 {http://jmvidal.cse.sc.edu/library/vu06a.pdf},
  cluster = 	 {7552508310033434477},
  keywords = 	 {multiagent learning}
}