@InProceedings{Supelec625,
author = {Senthilkumar Chandramohan and Matthieu Geist and Olivier Pietquin},
title = {{Sparse Approximate Dynamic Programming for Dialog Management}},
year = {2010},
booktitle = {{Proceedings of the 11th SIGDial Conference on Discourse and Dialogue}},
publisher = {ACL},
pages = {107-115},
month = {September},
address = {Tokyo (Japan)},
url = {http://www.sigdial.org/workshops/workshop11/proc/pdf/SIGDIAL22.pdf},
abstract = {Spoken dialogue management strategy optimization by means of
Reinforcement Learning (RL) is now part of the state of the
art. Yet, there is still a clear mismatch between the
complexity implied by the required naturalness of dialogue
systems and the inability of standard RL algorithms to scale
up. Another issue is the sparsity of the data available for
training in the dialogue domain which can not ensure
convergence of most of RL algorithms.
In this paper, we propose to combine a sample-efficient
generalization framework for RL with a feature selection
algorithm for the learning of an optimal spoken dialogue
management strategy.}
}