Agent-oriented incremental activity recognition for human teams
Daniele Masato, Timothy Norman, Wamberto Vasconcelos and Katia Sycara
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.