Agent-oriented incremental team and activity recognition

Authors:
Daniele Masato;Timothy J. Norman;Wamberto W. Vasconcelos;Katia Sycara
Affiliations:
Department of Computing Science, University of Aberdeen, Scotland, UK;Department of Computing Science, University of Aberdeen, Scotland, UK;Department of Computing Science, University of Aberdeen, Scotland, UK;Robotics Institute, Carnegie Mellon University, Pittsburgh, PA
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Year:
2011

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Abstract

Monitoring team activity is beneficial when human teams cooperate in the enactment of a joint plan. Monitoring allows teams to maintain awareness of each other's progress within the plan and it enables anticipation of information needs. Humans find this difficult, particularly in time-stressed and uncertain environments. In this paper we introduce a probabilistic model, based on Conditional Random Fields, to automatically recognise the composition of teams and the team activities in relation to a plan. The team composition and activities are recognised incrementally by interpreting a stream of spatio-temporal observations.