A Framework for Unsupervised Learning of Dialogue Strategies

This book addresses the problems of spoken dialogue system design and
especially automatic learning of optimal strategies for man-machine dialogues.
Besides the description of the learning methods, this text proposes a
framework for realistic simulation of human-machine dialogues based on
probabilistic techniques, which allows automatic evaluation and unsupervised
learning of dialogue strategies. This framework relies on stochastic modelling
of modules composing spoken dialogue systems as well as on user modelling.
Special care has been taken to build models that can either be hand-tuned or
learned from generic data.