Learning Influence among Interacting Markov Chains
Dong Zhang, Daniel Gática-Pérez, Samy Bengio, Deb Kumar Roy · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 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. In-dividual 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. 1