Understanding and efficiently representing skills is one of the most important problems in a general Programming by Demonstration (PbD) paradigm. We present Growing Hierarchical Dynamic Bayesian Networks (GHDBN), an adaptive variant of the general DBN model able to learn and to represent complex skills. The structure of the model, in terms of number of states and possible transitions between them, is not needed to be known a priori. Learning in the model is performed incrementally and in an unsupervised manner
Dindo, H., Schillaci, G. (2010). An adaptive probabilistic graphical model for representing skills in PbD settings. In PROCEEDINGS OF THE 5TH ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION (HRI 2010) (pp. 89-90) [10.1109/HRI.2010.5453257].
An adaptive probabilistic graphical model for representing skills in PbD settings
DINDO, Haris;
2010-01-01
Abstract
Understanding and efficiently representing skills is one of the most important problems in a general Programming by Demonstration (PbD) paradigm. We present Growing Hierarchical Dynamic Bayesian Networks (GHDBN), an adaptive variant of the general DBN model able to learn and to represent complex skills. The structure of the model, in terms of number of states and possible transitions between them, is not needed to be known a priori. Learning in the model is performed incrementally and in an unsupervised manner
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