Role-based teamwork activity recognition in observations of embodied agent actions

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Role-based teamwork activity recognition in observations of embodied agent actions

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International Conference on Autonomous Agents archive
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2 table of contents

Estoril, Portugal

SESSION: Agent cooperation table of contents

Pages 567-574

Year of Publication: 2008

ISBN:978-0-9817381-1-6

Authors

Linus J. Luotsinen	University of Central Florida, Orlando, Florida
Ladislau Bölöni	University of Central Florida, Orlando, Florida

Sponsors

AAAI : Association for the Advancement of Artifical Intelligence
ACM: Association for Computing Machinery

Publisher

International Foundation for Autonomous Agents and Multiagent Systems Richland, SC

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ABSTRACT

Recognizing team actions in the behavior of embodied agents has many practical applications and had seen significant progress in recent years. One approach with proven results is based on HMM-based recognition of spatio-temporal patterns in the behavior of the agents. While it had been shown to work on real-world datasets, this approach was found to be brittle.

In this paper we present two contributions which together can significantly increase the robustness of teamwork activity recognition. First we introduce a technique to reduce high dimensional continuous input data to a set of discrete features, which capture the essential components of the team actions. Second, we prefix the actual team action recognition with a role recognition module, which allows us to present the recognizer with arbitrarily shuffled input, and still obtain high recognition rates.

We validate the improved accuracy and robustness of the team action recognizer on datasets derived from captured real world data.

REFERENCES

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