@Article{haynes98a,
  author =	 {Thomas Haynes and Sandip Sen},
  title =	 {Learning cases to resolve conflicts and improve
                  group behavior},
  journal =	 {International Journal of Human-Computer Studies},
  year =	 1998,
  volume =	 48,
  number =	 1,
  pages =	 {31--49},
  abstract =	 {Groups of agents following fixed behavioral rules
                  can be limited in performance and
                  efficiency. Adaptability and flexibility are key
                  components of intelligent behavior which allow agent
                  groups to improve performance in a given domain
                  using prior problem-solving experience. We motivate
                  the utility of individual learning by group members
                  in the context of overall group behavior. In
                  particular, we propose a framework in which
                  individual group members learn cases from
                  problem-solving experiences to improve their model
                  of other group members. We use a testbed problem
                  from the Distributed Artificial Intelligence
                  literature to show that simultaneous learning by
                  group members can lead to significant improvement in
                  group performance and efficiency over agent groups
                  following static behavioral rules.},
  keywords =     {multiagent learning},
  url = 	 {http://jmvidal.cse.sc.edu/library/haynes98a.pdf},
  doi = 	 {10.1006/ijhc.1997.0159},
  googleid = 	 {StBS0TbA7XsJ:scholar.google.com/},
  cluster = 	 {8930004977818193994}
}