This book addresses the problem of pragmatic competence: how to improve the communicative skills of a system by providing effective mechanisms to develop better dialogue strategies.
Academic systems often aim to emulate human behaviour in order to generate ‘natural’ behaviour, whereas commercial systems are required to be robust interfaces in order to solve a specific task.
In industry the initial design is commonly motivated by guidelines and ‘best practises’ which should help to assure the system’s usability
Dialogue strategies developed in academia are usually extensively tested against some baseline in order to make scientific claims, e.g. by showing some significant differences in system behaviour.
Zue calls the dialogue system of the future an “organic interface”, that can learn, grow, re-congure, and repair itself.
In general, there are three major learning paradigms, each corresponding to a particular abstract learning task:
To date, different Machine Learning approaches have been applied to automatic dialogue management:
Action selection is guided by the following optimisation:
In contrast to the above approaches, Reinforcement Learning treats dialogue strategy learning as a sequential optimisation problem, leading to strategies which are globally optimal