Learning Influence among Interacting Markov Chains

  title={Learning Influence among Interacting Markov Chains},
  author={Dong Zhang and Daniel Gatica-Perez and Samy Bengio and Deb Roy},
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. 
Highly Cited
This paper has 51 citations. REVIEW CITATIONS
33 Citations
14 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 33 extracted citations

51 Citations

Citations per Year
Semantic Scholar estimates that this publication has 51 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 14 references

Similar Papers

Loading similar papers…