Agent-Oriented Incremental Team and Activity Recognition
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1402-1406.
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.
@inproceedings{ijcai2011p237,
title = {Agent-Oriented Incremental Team and Activity Recognition},
author = {Masato, Daniele and Norman, Timothy J. and Vasconcelos, Wamberto Weber and Sycara, Katia P.},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1402--1406},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-237},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-237},
}
TY - CPAPER TI - Agent-Oriented Incremental Team and Activity Recognition AU - Masato, Daniele AU - Norman, Timothy J. AU - Vasconcelos, Wamberto Weber AU - Sycara, Katia P. T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1402 EP - 1406 DO - 10.5591/978-1-57735-516-8/IJCAI11-237 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-237 ER -Copied
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