---
title: "Agent-Oriented Incremental Team and Activity Recognition"
authors:
  - "Daniele Masato"
  - "Timothy J. Norman"
  - "Wamberto Weber Vasconcelos"
  - "Katia P. Sycara"
doi: "10.5591/978-1-57735-516-8/IJCAI11-237"
url: "https://ijcai.org/proceedings/2011/237"
pdf: "https://ijcai.org/proceedings/2011/0237.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "1402-1406"
track: ""
keywords:
---

# Agent-Oriented Incremental Team and Activity Recognition

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

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 1402-1406. DOI: [10.5591/978-1-57735-516-8/IJCAI11-237](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-237)

## 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.