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Human-Computer Interfaces are now widely studied and become one of the major interests among the scientific community. More and more electronic devices surround people in their day-to-day life. This exponential incursion of electronics in homes, cars and work places is not only due to its ability to ease the achievement of common and boring tasks or the continuously decreasing prices but also because the increasing “user-friendliness” of interfaces makes it easier to use. Being studied for more than fifty years, speech and natural language processing knew major progresses during the two last decades. It is now feasible to build real Spoken Dialogue Systems (SDS) interacting with human users through voice-enabled interactions. Speech often appears as a natural way to interact for a human being and it provides potential benefits such as hand-free access to machines, ergonomics and greater efficiency of interaction. Yet, speech-based interfaces design has been an expert job for a long time. It necessitates good skills in speech technologies and low-level programming. Moreover, rapid design and reusability of previously designed systems are almost impossible. For these reasons, but not only, people are less used to interact with speech-based interfaces which are therefore thought as less intuitive. Designing and optimizing a SDS is not only the combination of speech and language processing systems such as Automatic Speech Recognition (ASR) (Rabiner & Juang 1993), Spoken Language Understanding (SLU) (Allen 1998), Natural Language Generation (NLG) (Reiter & Dale 2000), and Text-to-Speech (TTS) synthesis (Dutoit 1997) systems. It also requires the development of dialogue strategies taking at least into account the performances of these subsystems (and others), the nature of the task (e.g. form filling (Pietquin & Dutoit 2006a), tutoring (Graesser et al 2001), robot control, or database querying (Pietquin 2006b)), and the user’s behaviour (e.g. cooperativeness, expertise (Pietquin 2004)). The great variability of these factors makes rapid design of dialogue strategies and reusability across tasks of previous work very complex. Most often, such a design is a cyclic process composed of strategy hand-coding, prototype releases and expansive and time consuming user tests. In addition, there is also no guarantee that hand-crafted strategies developed by experts are anything close to optimal. Because it provides data-driven methods and objective clues about performances, statistical learning of optimal dialogue strategies became a leading domain of research (Lemon & Pietquin, 2007). The goal of such

Optimising Spoken Dialogue Strategies within the Reinforcement Learning Paradigm