@InProceedings{Supelec856,
author = {Lucie Daubigney and Matthieu Geist and Olivier Pietquin},
title = {{Model-free POMDP optimisation of tutoring systems with echo-state networks}},
year = {2013},
booktitle = {{Proceedings of the 14th SIGDial Meeting on Discourse and Dialogue (SIGDial 2013)}},
pages = {102-106},
month = {August},
address = {Metz (France)},
url = {http://www.sigdial.org/workshops/conference14/proceedings/pdf/SIGDIAL14.pdf},
abstract = {Intelligent Tutoring Systems (ITSs) are now recognised as an
interesting alternative for providing learning opportunities
in various domains. The Reinforcement Learning (RL) approach has
been shown reliable for finding efficient teaching strategies.
However, similarly to other human-machine interaction systems
such as spoken dialogue systems, ITSs suffer from a partial
knowledge of the interlocutor’s intentions. In the dialogue
case, engineering work can infer a precise state of the user by
taking into account the uncertainty provided by the spoken
understanding language module. A model-free approach based on RL
and Echo State Newtorks (ESNs), which retrieves similar
information, is proposed here for tutoring.}
}