Analyzing Team Decision-Making in Tactical Scenarios

    Authors

    G. Sukthankar;K. Sycara

    Comments

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    Abbreviated Journal Title

    Comput. J.

    Keywords

    pattern recognition; teamwork; multi-player games; evidential reasoning; JAYWALKING; Computer Science, Hardware & Architecture; Computer Science, Information; Systems; Computer Science, Software Engineering; Computer Science, ; Theory & Methods

    Abstract

    Team decision-making is a bundle of interdependent activities that involve gathering, interpreting and exchanging information; creating and identifying alternative courses of action; choosing among alternatives by integrating the often different perspectives of team members and implementing a choice and monitoring its consequences. To accomplish joint tasks, human team members often assume distinctive roles in task completion. We believe that to design and build software agents that can assist human teams, we need develop automated techniques to identify the roles of the human decision-makers. If the supporting agents are insensitive to shifts in the team's roles, they cannot effectively monitor the team's activities. This article addresses the problem of doing offline role analysis of battle scenarios from multi-player team games. The ability to identify team roles from observations is important for a wide range of applications including automated commentary generation, game coaching and opponent modeling. We define a role as a preference model over possible actions based on the game state. This article explores two promising approaches for automated role analysis: (1) a model-based system for combining evidence from observed events using the Dempster-Shafer theory and (2) a data-driven discriminative classifier using support vector machines.

    Journal Title

    Computer Journal

    Volume

    53

    Issue/Number

    5

    Publication Date

    1-1-2010

    Document Type

    Article

    Language

    English

    First Page

    503

    Last Page

    512

    WOS Identifier

    WOS:000278227500003

    ISSN

    0010-4620

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