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.
|Title of host publication||Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence|
|Place of Publication||Menlo Park, California|
|Number of pages||6|
|ISBN (Print)||978-1-57735-512-0, 978-1-57735-514-4|
|Publication status||Published - 2011|
Masato, D., Norman, T. J., Vasconcelos, W. W., & Sycara, K. (2011). Agent-oriented Incremental Team and Activity Recognition. In T. Walsh (Ed.), Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (pp. 1402-1407). AAAI Press. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-237