Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/HRI.2010.5453257 | Human-Robot Interaction |
Keywords | Field | DocType |
pbd setting,unsupervised manner,adaptive variant,general programming,important problem,adaptive probabilistic graphical model,complex skill,general dbn model,possible transition,hierarchical dynamic bayesian networks,generic programming,adaptive systems,human robot interaction,clustering algorithms,hidden markov models,dynamic bayesian networks,probability distribution,unsupervised learning,encoding,bayesian methods,dynamic bayesian network,machine learning,probabilistic logic,computational modeling,graphical models,acceleration | Programming by demonstration,Computer science,A priori and a posteriori,Unsupervised learning,Artificial intelligence,Probabilistic logic,Graphical model,Hidden Markov model,Machine learning,Dynamic Bayesian network,Automatic programming | Conference |
ISSN | ISBN | Citations |
2167-2121 | 978-1-4244-4893-7 | 2 |
PageRank | References | Authors |
0.43 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haris Dindo | 1 | 125 | 17.49 |
| Guido Schillaci | 2 | 69 | 10.45 |