Learning Influence among Interacting Markov Chains

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. Experiments on synthetic multi-player games and a multi-party meeting corpus show the effectiveness of the proposed model.