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. 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.
- URL: http://publications.idiap.ch/downloads/reports/2005/zhang-nips-05.pdf
- Related documents: http://publications.idiap.ch/index.php/publications/showcite/zhang-rr-05-48
Record created on 2006-03-10, modified on 2016-08-08