@InCollection{Supelec12,
author = {Olivier Pietquin},
title = {{Machine Learning for Spoken Dialogue Management: an Experiment with Speech-Based Database Querying}},
year = {2006},
booktitle = {{Artificial Intelligence : Methodology, Systems \& Applications}},
publisher = {Springer Verlag},
volume = {4183},
pages = {172-180},
editor = {J. Euzenat \& J. Domingue},
series = {Lecture Notes in Artificial Intelligence},
url = {http://dx.doi.org/10.1007/11861461_19},
abstract = {Although speech and language processing techniques achieved a
relative maturity during the last decade, designing a spoken
dialogue system is still a tailoring task because of the great
variability of factors to take into account. Rapid design and
reusability across tasks of previous work is made very
difficult. For these reasons, machine learning methods applied
to dialogue strategy optimization has become a leading subject
of researches since the mid 90’s. In this paper, we describe an
experiment of reinforcement learning applied to the
optimization of speech-based database querying. We will
especially emphasize on the sensibility of the method
relatively to the dialogue modeling parameters in the framework
of the Markov decision processes, namely the state space and
the reinforcement signal. The evolution of the design will be
exposed as well as results obtained on a simple real
application. },
hal = {hal-00208016}
}