Analyzing Team Decision Making in Tactical Scenarios

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.