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@InProceedings{AIPS02-STN,
  author =	 {Patrick Riley and Manuela Veloso},
  title =	 {Planning for Distributed Execution Through Use of
                  Probabilistic Opponent Models},
  booktitle =	 aips2002,
  year =	 2002,
  pages = 	 {72--81},
  note = 	 {{\it Best Paper Award}},
  wwwnote =	 {<a href="http://www.aaai.org">Publisher (AAAI) Website</a>},
  abstract =	 { In multiagent domains with adversarial and
                  cooperative team agents, team agents should be
                  adaptive to the current environment and opponent. We
                  introduce an online method to provide the agents
                  with team plans that a ``coach'' agent generates in
                  response to the specific opponents. The coach agent
                  can observe the agents' behaviors but it has only
                  periodic communication with the rest of the
                  team. The coach uses a Simple Temporal Network to
                  represent team plans as coordinated movements among
                  the multiple agents and the coach searches for an
                  opponent-dependent plan for its teammates. This plan
                  is then communicated to the agents, who execute the
                  plan in a distributed fashion, using information
                  from the plan to maintain consistency among the team
                  members. In order for these plans to be effective
                  and adaptive, models of opponent movement are used
                  in the planning. The coach is then able to quickly
                  select between different models online by using a
                  Bayesian style update on a probability distribution
                  over the models. Planning then uses the model which
                  is found to be the most likely. The system is fully
                  implemented in a simulated robotic soccer
                  environment. In several recent games with completely
                  unknown adversarial teams, the approach demonstrated
                  a visible adaptation to the different teams. },
  bib2html_pubtype = {Refereed Conference,Award Winner},
  bib2html_rescat = {Planning,Coaching},
  bib2html_funding = {NSF,CoABS,ActiveTemplates},
}