Learning Influence Among Interacting Markov Chains
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.
Cite
Text
Zhang et al. "Learning Influence Among Interacting Markov Chains." Neural Information Processing Systems, 2005.Markdown
[Zhang et al. "Learning Influence Among Interacting Markov Chains." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/zhang2005neurips-learning-a/)BibTeX
@inproceedings{zhang2005neurips-learning-a,
title = {{Learning Influence Among Interacting Markov Chains}},
author = {Zhang, Dong and Gatica-perez, Daniel and Bengio, Samy and Roy, Deb},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {1577-1584},
url = {https://mlanthology.org/neurips/2005/zhang2005neurips-learning-a/}
}