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.


Published in:
NIPS
Presented at:
NIPS
Year:
2005
Keywords:
Note:
IDIAP-RR 05-48
Laboratories:




 Record created 2006-03-10, last modified 2018-03-17

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