Learning Influence among Interacting Markov Chains

@inproceedings{Zhang2005LearningIA,
  title={Learning Influence among Interacting Markov Chains},
  author={Dong Zhang and Daniel Gatica-Perez and Samy Bengio and Deb Roy},
  booktitle={NIPS},
  year={2005}
}
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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