Particle Swarm Optimisation of Spoken Dialogue System Strategies

Lucie Daubigney 1, 2 Matthieu Geist 3 Olivier Pietquin 2
1 MAIA - Autonomous intelligent machine
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
2 IMS - Equipe Information, Multimodalité et Signal
UMI2958 - Georgia Tech - CNRS [Metz], SUPELEC-Campus Metz
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.
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Lucie Daubigney, Matthieu Geist, Olivier Pietquin. Particle Swarm Optimisation of Spoken Dialogue System Strategies. Interspeech 2013, Aug 2013, Lyon, France. pp.1-5. ⟨hal-00916935⟩

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