PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Influence Among Interacting Markov Chains
Dong Zhang, Daniel Gatica-Perez, Samy Bengio and Deb Roy
In: Advances in Neural Information Processing Systems, NIPS, 2005, Vancouver, Canada.


We present a model that learns the influence of interacting Markov chains within a team. The proposed model is a dynamic Bayesian network (DBN) with a two-level structure: individual-level and group-level. Individual level models actions of each player, and the group-level models actions of the team as a whole. Experiments on synthetic multi-player games and a multi-party meeting corpus show the effectiveness of the proposed model.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Multimodal Integration
ID Code:1104
Deposited By:Samy Bengio
Deposited On:26 September 2005