Modeling and Inference with Relational Dynamic Bayesian Networks

Authors:
Cristina Manfredotti
Affiliations:
DISCo Comp. Science Dept, University of Milano-Bicocca, Milano, Italy 20126 and Dept. Computer Science, University of Toronto, Toronto, Canada M5S3G4
Venue:
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2009

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Abstract

The explicit recognition of the relationships between interacting objects can improve the understanding of their dynamic domain. In this work, we investigate the use of Relational Dynamic Bayesian Networks to represent the dependencies between the agents' behaviors in the context of multi-agents tracking. We propose a new formulation of the transition model that accommodates for relations and we extend the Particle Filter algorithm in order to directly track relations between the agents. Many applications can benefit from this work, including terrorist activities recognition, traffic monitoring, strategic analysis and sports.