Title :
An adaptive probabilistic graphical model for representing skills in PbD settings
Author :
Dindo, Haris ; Schillaci, Guido
Author_Institution :
Dipt. di Ing. Inf., Univ. of Palermo, Palermo, Italy
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.
Keywords :
automatic programming; belief networks; human-robot interaction; robot programming; unsupervised learning; adaptive probabilistic graphical model; growing hierarchical dynamic Bayesian network; imitation learning; programming by demonstration; skill representation; unsupervised learning; Acceleration; Adaptive systems; Bayesian methods; Clustering algorithms; Collaborative work; Encoding; Graphical models; Hidden Markov models; Human robot interaction; Robot programming; Dynamic Bayesian Network; Growing Hierarchical Dynamic Bayesian Network; Imitation Learning; Incremental Learning; Machine Learning;
Conference_Titel :
Human-Robot Interaction (HRI), 2010 5th ACM/IEEE International Conference on
Conference_Location :
Osaka
Print_ISBN :
978-1-4244-4892-0
Electronic_ISBN :
978-1-4244-4893-7
DOI :
10.1109/HRI.2010.5453257
