author = {Senthilkumar Chandramohan and Olivier Pietquin},
title = {User and Noise Adaptive Dialogue Management Using Hybrid System Actions},
year = {2010},
booktitle = {Spoken Dialogue Systems for Ambient Environments},
publisher = {Springer Verlag, Heidelberg - Berlin},
volume = {6392},
pages = {13-24},
month = {October},
note = {Proceedings of the International Workshop on Spoken Dialogue Systems (IWSDS 2010)},
editor = {Gary Geunbae Lee and Joseph Mariani and Wolfgang Minker and Satoshi Nakamura},
series = {Lecture Notes in Artificial Intelligence (LNAI)},
address = {Gotemba, Shizuoka (Japan)},
url = {http://www.metz.supelec.fr//metz/personnel/pietquin/pdf/IWSDS_2010_SCOP.pdf},
isbn = {978-3-642-16201-5},
abstract = {In recent years reinforcement-learning-based approaches have been widely used for management policy optimization in spoken dialogue systems (SDS). A dialogue management policy is a mapping from dialogue states to system actions, i.e. given the state of the dialogue the dialogue policy determines the next action to be performed by the dialogue manager. So-far policy optimization primarily focused on mapping the dialogue state to simple system actions (such as confirm or ask one piece of information) and the possibility of using complex system actions (such as confirm or ask several slots at the same time) has not been well investigated. In this paper we explore the possibilities of using complex (or hybrid) system actions for dialogue management and then discuss the impact of user experience and channel noise on complex action selection. Our experimental results obtained using simulated users reveal that user and noise adaptive hybrid action selection can perform better than dialogue policies which can only perform simple actions.}