Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that
reduces the cost of laboriously handcrafting complex dialog managers and that provides …

Dialogue policy learning (DPL) is a key component in a task-oriented dialogue (TOD)
system. Its goal is to decide the next action of the dialogue system, given the dialogue state …

Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …

Dialog policy decides what and how a task-oriented dialog system will respond, and plays a
vital role in delivering effective conversations. Many studies apply Reinforcement Learning …

Our aim is to build listening agents that attentively listen to their users and satisfy their desire
to speak and have themselves heard. This article investigates how to automatically create a …

An estimated 11% of adults report experiencing some form of cognitive decline, which may
be associated with conditions such as stroke or dementia and can impact their memory …

This paper reports theoretical and empirical results obtained for the score-based Inverse
Reinforcement Learning (IRL) algorithm. It relies on a non-standard setting for IRL consisting …

This thesis presents a new modelling framework for dialogue management based on the
concept of probabilistic rules. Probabilistic rules are defined as if... then... else constructions …

Reinforcement learning is now an acknowledged approach for optimizing the interaction
strategy of spoken dialogue systems. If the first considered algorithms were quite basic (like …

Dialog systems that realize dialog control with reinforcement learning have recently been
proposed. However, reinforcement learning has an open problem that it requires a reward …