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.


Publié dans:
NIPS
Présenté à:
NIPS
Année
2005
Mots-clefs:
Note:
IDIAP-RR 05-48
Laboratoires:




 Notice créée le 2006-03-10, modifiée le 2019-12-05

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