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Model-free POMDP optimisation of tutoring systems with echo-state networks

Identifieur interne : 003267 ( Hal/Corpus ); précédent : 003266; suivant : 003268

Model-free POMDP optimisation of tutoring systems with echo-state networks

Auteurs : Lucie Daubigney ; Matthieu Geist ; Olivier Pietquin

Source :

RBID : Hal:hal-00869773

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.

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Hal:hal-00869773

Le document en format XML

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<div type="abstract" xml:lang="en">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.</div>
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