Particle Swarm Optimisation of Spoken Dialogue System Strategies
Identifieur interne : 001057 ( Main/Exploration ); précédent : 001056; suivant : 001058Particle Swarm Optimisation of Spoken Dialogue System Strategies
Auteurs : Lucie Daubigney [France] ; Matthieu Geist [France] ; Olivier Pietquin [France]Source :
Descripteurs français
Abstract
Dialogue management optimisation has been cast into a plan- ning under uncertainty problem for long. Some methods such as Reinforcement Learning (RL) are now part of the state of the art. Whatever the solving method, strong assumptions are made about the dialogue system properties. For instance, RL assumes that the dialogue state space is Markovian. Such con- straints may involve important engineering work. This paper introduces a more general approach, based on fewer modelling assumptions. A Black Box Optimisation (BBO) method and more precisely a Particle Swarm Optimisation (PSO) is used to solve the control problem. In addition, PSO allows taking ad- vantage of the parallel aspect of the problem of optimising a system online with many users calling at the same time. Some preliminary results are presented.
Url:
Affiliations:
- France
- Franche-Comté, Grand Est, Lorraine (région)
- Besançon, Metz, Nancy
- Université Paul Verlaine - Metz, Université de Bourgogne Franche-Comté, Université de Franche-Comté, Université de Lorraine
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Le document en format XML
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Metz</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>black-box optimisation</term><term>dialogue management</term><term>spoken dialogue system</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Dialogue management optimisation has been cast into a plan- ning under uncertainty problem for long. Some methods such as Reinforcement Learning (RL) are now part of the state of the art. Whatever the solving method, strong assumptions are made about the dialogue system properties. For instance, RL assumes that the dialogue state space is Markovian. Such con- straints may involve important engineering work. This paper introduces a more general approach, based on fewer modelling assumptions. A Black Box Optimisation (BBO) method and more precisely a Particle Swarm Optimisation (PSO) is used to solve the control problem. In addition, PSO allows taking ad- vantage of the parallel aspect of the problem of optimising a system online with many users calling at the same time. Some preliminary results are presented.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Franche-Comté</li><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Besançon</li><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université Paul Verlaine - Metz</li><li>Université de Bourgogne Franche-Comté</li><li>Université de Franche-Comté</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Daubigney, Lucie" sort="Daubigney, Lucie" uniqKey="Daubigney L" first="Lucie" last="Daubigney">Lucie Daubigney</name></region><name sortKey="Geist, Matthieu" sort="Geist, Matthieu" uniqKey="Geist M" first="Matthieu" last="Geist">Matthieu Geist</name><name sortKey="Pietquin, Olivier" sort="Pietquin, Olivier" uniqKey="Pietquin O" first="Olivier" last="Pietquin">Olivier Pietquin</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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