Agent-Oriented Incremental Team and Activity Recognition
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.
Cite
Text
Masato et al. "Agent-Oriented Incremental Team and Activity Recognition." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-237Markdown
[Masato et al. "Agent-Oriented Incremental Team and Activity Recognition." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/masato2011ijcai-agent/) doi:10.5591/978-1-57735-516-8/IJCAI11-237BibTeX
@inproceedings{masato2011ijcai-agent,
title = {{Agent-Oriented Incremental Team and Activity Recognition}},
author = {Masato, Daniele and Norman, Timothy J. and Vasconcelos, Wamberto Weber and Sycara, Katia P.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2011},
pages = {1402-1407},
doi = {10.5591/978-1-57735-516-8/IJCAI11-237},
url = {https://mlanthology.org/ijcai/2011/masato2011ijcai-agent/}
}