@inproceedings{2000216,
author={Elias Gyftodimos and Peter Flach},
title={Learning Hierarchical Bayesian Networks for Human Skill Modelling},
booktitle={Proceedings of the 2003 UK Workshop on Computational Intalligence (UKCI-2003)},
editor={Jonathan Rossiter and Trevor Martin},
ISBN={0862925371},
publisher={University of Bristol},
pages={55--62},
month={September},
year={2003},
abstract={In previous work we have proposed Hierarchical Bayesian Networks (HBNs)
as an extension of Bayesian Networks. HBNs are able to deal with structured
domains, and use knowledge about the structure of the data to
introduce a bias that can contribute to improving
inference and learning methods. In effect,
nodes in an HBN are (possibly nested)
aggregations of simpler nodes. Every aggregate node is itself
an HBN modelling independencies
inside a subset of the whole world under consideration.
In this paper we introduce inference in HBNs using a
stochastic sampling algorithm, and a learning method for HBNs
based on the Cooper and Herskovits structure likelihood
measure.
We furthermore explore how HBNs can be applied
to the problem of modelling right arm motion in cello playing.
This problem is inherently hierarchical and therefore well-suited for
modelling by HBNs. The task is to
construct a descriptive model for a player's movements
observing the position of different joints as well as
muscular activity of the right arm during the execution of
a short musical extract.
%We demonstrate how the learning algorithm we propose efficiently
%constructs a model for the given data.
Different datasets were used to construct models both for
an amateur and a professional cello player, and
differences between the derived HBNs can be used to interpret
the differences on each person's ``tacit knowledge" on the task.},
abstract-url={http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=2000216},
url={http://www.cs.bris.ac.uk/Publications/Papers/2000216.pdf},
keyword={Artificial Intelligence,Machine Learning},
pubtype={102}
}