Haris Dindo
Guido Schillaci
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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Digital Library
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- Vasquez, Fraichard, and Laugier. Incremental learning of statistical motion patterns with growing hidden markov models, 2007. Transactions on intelligent transportation systems. Google ScholarDigital Library
Index Terms
- An adaptive probabilistic graphical model for representing skills in pbd settings
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