Learning Influence among Interacting Markov Chains

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

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Authors

Dong Zhang, Daniel Gatica-perez, Samy Bengio, Deb Roy

Abstract

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.