We describe an evaluation of spoken dialogue strategies designed using hierarchical reinforcement learning agents. The dialogue strategies were learnt in a simulated environment and tested in a laboratory setting with 32 users. These dialogues were used to evaluate three types of machine dialogue behaviour: hand-coded, fully-learnt and semi-learnt. These experiments also served to evaluate the ...

We present an approach to adapt dynamically the language models (LMs) used by a speech recognizer that is part of a spoken dialogue system. We have developed a grammar generation strategy that automatically adapts the LMs using the semantic information that the user provides (represented as dialogue concepts), together with the information regarding the intentions of the speaker (inferred by th...

Since 2011, the region has been a profound socio-economic changes originated from n Tunisia, & spread to Middle Eastern and the former power structures affected. The management and direction to these uprisings is the key question of this paper. The key question, is the foreign policy of the Islamic Republic of Iran how answer to these Middle East public uprisings in the years 2011-2014? <...

Public sector organisations have different communication needs from private sector organisations. For example, they quite often have programmes aimed at behaviour changes, challenging their ability to choose the strategy which best suit their purpose. Dialogue is often considered a strategic tool which can lead to organisational gains such as motivation, learning, development, collaboration, et...

This paper reports research concerning issues involved in adopting a computational dialectics approach to develop a human-computer dialogue system for educational debate. In particular, we propose a dialogue model and a set of computationally usable strategic heuristics to enable the computer to engage its users in debate on a controversial issue. The system operationalising the proposed dialog...

Application of reinforcement learning methods in the development of dialogue strategies that support robust and efficient human–computer interaction using spoken language is a growing research area. In spoken dialogue system, Markov Decision Processes (MDPs) provide a formal framework for making dialogue management decisions for planning. This framework enables the system to learn the value of ...

This paper explores dialogue adaptation over repeated interactions within a taskoriented human tutorial dialogue corpus. We hypothesize that over the course of four tutorial dialogue sessions, tutors adapt their strategies based on the personality of the student, and in particular to student introversion or extraversion. We model changes in strategy over time and use them to predict how effecti...

Recent years have seen a growing recognition of the importance of affect in learning. Efforts are being undertaken to enable intelligent tutoring systems to recognize and respond to learner emotion, but the field has not yet seen the emergence of a fully contextualized model of learner affect. This paper reports on a study of learner affect through an analysis of facial expression in human task...