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Carnegie Mellon Robotics Institute

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Abstract

This paper describes a methodology for recording, representing, and recognizing team behaviors performed by players in an Unreal Tournament MOUT (Military Operations in Urban Terrain) scenario. To directly monitor the performance of human players, we developed a customized version of Unreal Tournament that records position and orientation of all the team members through time as they participate in a simulated MOUT scenario of a firing team moving through an urban area. Behavior recognition is performed offline using a set of hidden Markov models on short movement sequences that are translated into a canonical reference frame; the behavior model with the highest log likelihood for a given sequence is identified as correct. We believe that accurate offline recognition of team behaviors is an important prerequisite towards building virtual training environments for teamwork tasks.

Keywords

plan recognition, teamwork, hidden Markov models

Notes

Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems

Associated Lab(s) / Group(s): Advanced Agent - Robotics Technology Lab

Associated Project(s): Agent-based Composition of Behavioral Models

Number of pages: 6

Text Reference

Gita Sukthankar and Katia Sycara, "Automatic Recognition of Human Team Behaviors," Proceedings of Modeling Others from Observations (MOO), Workshop at the International Joint Conference on Artificial Intelligence (IJCAI), July, 2005

BibTeX Reference

@incollection{Sukthankar_2005_5031,
   author = "Gita Sukthankar and Katia Sycara",
   title = "Automatic Recognition of Human Team Behaviors",
   booktitle = "Proceedings of Modeling Others from Observations (MOO), Workshop at the International Joint Conference on Artificial Intelligence (IJCAI)",
   month = "July",
   year = "2005",
}

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