Vidal's library
Title: Learning cases to resolve conflicts and improve group behavior
Author: Thomas Haynes and Sandip Sen
Journal: International Journal of Human-Computer Studies
Volume: 48
Number: 1
Pages: 31--49
Year: 1998
DOI: 10.1006/ijhc.1997.0159
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.

Cited by 16  -  Google Scholar

@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}
}
Last modified: Wed Mar 9 10:14:38 EST 2011