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Recognizing Probabilistic Opponent Movement Models

Patrick Riley and Manuela Veloso. Recognizing Probabilistic Opponent Movement Models. In A. Birk, S. Coradeschi, and S. Tadokoro, editors, RoboCup-2001: Robot Soccer World Cup V, number 2377 in Lecture Notes in Artificial Intelligence, pp. 453–458, Springer Verlag, Berlin, 2002. (extended abstract)
Publisher's Webpage© Springer-Verlag

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Abstract

In multiagent adversarial domains, team agents should adapt to the environment and opponent. We introduce a model representation as part of a planning process for a simulated soccer domain. The planning is centralized, but the plans are executed in a multi-agent environment, with teammate and opponent agents. Further, we present a recognition algorithm where the model which most closely matches the behavior of the opponents can be selected from few observations of the opponent. Empirical results are presented to verify that important information is maintained through the abstraction the models provide.

BibTeX Entry

@InCollection(LNAI01-coach,
  Author =	 "Patrick Riley and Manuela Veloso",
 Title =	 "Recognizing Probabilistic Opponent Movement Models",
  booktitle =	 "{R}obo{C}up-2001: Robot Soccer World Cup {V}",
  Editor =	 "A. Birk and S. Coradeschi and S. Tadokoro",
  Publisher =	 "Springer Verlag",
  series = 	 {Lecture Notes in Artificial Intelligence},
  number = 	 {2377},
  pages = 	 {453--458},
  address =	 "Berlin",
  year =	 "2002",
  note = 	 {(extended abstract)},
  wwwnote =      {<a href="http://www.springer.de/comp/lncs/index.html">Publisher's Webpage</a>&copy Springer-Verlag},
  abstract =	 {In multiagent adversarial domains, team agents
                  should adapt to the environment and opponent. We
                  introduce a model representation as part of a
                  planning process for a simulated soccer domain. The
                  planning is centralized, but the plans are executed
                  in a multi-agent environment, with teammate and
                  opponent agents. Further, we present a recognition
                  algorithm where the model which most closely matches
                  the behavior of the opponents can be selected from
                  few observations of the opponent. Empirical results
                  are presented to verify that important information
                  is maintained through the abstraction the models
                  provide.},
  bib2html_pubtype = {Refereed Conference},
  bib2html_rescat = {Coaching,Opponent and Teammate Modeling},
  bib2html_funding = {NSF,CoABS,ActiveTemplates},
)

Generated by bib2html.pl (written by Patrick Riley ) on Tue Oct 09, 2007 00:00:13

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