Agent-oriented Incremental Team and Activity Recognition

Daniele Masato, Timothy J Norman, Wamberto Weber Vasconcelos, Katia Sycara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Second International Joint Conference on Artificial Intelligence
EditorsToby Walsh
Place of PublicationMenlo Park, California
PublisherAAAI Press
Pages1402-1407
Number of pages6
ISBN (Electronic)978-1-57735-516-8
ISBN (Print)978-1-57735-512-0, 978-1-57735-514-4
DOIs
Publication statusPublished - 2011

    Fingerprint

Cite this

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