Learning influence among interacting Markov chains
| Type of publication: | Conference paper |
| Citation: | zhang-rr-05-48b |
| Booktitle: | NIPS |
| Year: | 2005 |
| Note: | IDIAP-RR 05-48 |
| Crossref: | zhang-rr-05-48: |
| 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. |
| Userfields: | ipdmembership={vision}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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