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.
|EPrint Type:||Conference or Workshop Item (Poster)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Samy Bengio|
|Deposited On:||26 September 2005|