Towards the Use of Dialog Systems to Facilitate Inclusive Education

Continuous advances in the development of information technologies have currently led to the possibility of accessing learning contents from anywhere, at anytime, and almost instantaneously. However, accessibility is not always the main objective in the design of educative applications, specifically to facilitate their adoption by disabled people. Different technologies have recently emerged to foster the accessibility of computers and new mobile devices, favoring a more natural communication between the student and the developed educative systems. This chapter describes innovative uses of multimodal dialog systems in education, with special emphasis in the advantages that they provide for creating inclusive applications and learning activities

Evolutionary Reinforcement Learning of Spoken Dialogue Strategies

Institute for Communicating and Collaborative SystemsFrom a system developer's perspective, designing a spoken dialogue system can be a time-consuming and difficult process. A developer may spend a lot of time anticipating how a potential user might interact with the system and then deciding on the most appropriate system response. These decisions are encoded in a dialogue strategy, essentially a mapping between anticipated user inputs and appropriate system outputs. To reduce the time and effort associated with developing a dialogue strategy, recent work has concentrated on modelling the development of a dialogue strategy as a sequential decision problem. Using this model, reinforcement learning algorithms have been employed to generate dialogue strategies automatically. These algorithms learn strategies by interacting with simulated users. Some progress has been made with this method but a number of important challenges remain. For instance, relatively little success has been achieved with the large state representations that are typical of real-life systems. Another crucial issue is the time and effort associated with the creation of simulated users. In this thesis, I propose an alternative to existing reinforcement learning methods of dialogue strategy development. More specifically, I explore how XCS, an evolutionary reinforcement learning algorithm, can be used to find dialogue strategies that cover large state spaces. Furthermore, I suggest that hand-coded simulated users are sufficient for the learning of useful dialogue strategies. I argue that the use of evolutionary reinforcement learning and hand-coded simulated users is an effective approach to the rapid development of spoken dialogue strategies. Finally, I substantiate this claim by evaluating a learned strategy with real users. Both the learned strategy and a state-of-the-art hand-coded strategy were integrated into an end-to-end spoken dialogue system. The dialogue system allowed real users to make flight enquiries using a live database for an Edinburgh-based airline. The performance of the learned and hand-coded strategies were compared. The evaluation results show that the learned strategy performs as well as the hand-coded one (81% and 77% task completion respectively) but takes much less time to design (two days instead of two weeks). Moreover, the learned strategy compares favourably with previous user evaluations of learned strategies

Systems Engineering Solution

The development and realisation of urban infrastructural projects such as bridges is getting increasingly more difficult and complex to manage. The challenge for the actors to develop an effective solution for the project within the traditional dimensions of time, budget and quality is still very present. But society also calls for more sustainable solutions which minimizes an eventual negative impact on the environment and takes into account the interests of stakeholders. The introduction of national and EU regulated procurement methods such as the Most Economically Advantageous Tenders (MEAT), has opened the way towards a more active and balanced involvement of actors and stakeholders in the development and realisation of urban construction projects. In this new environment, the client, consultant, contractor and stakeholders strive to work together in order to realise a sustainable solution. This document describes a methodology for the management of project processes with the aim of achieving a lawful, effective and sustainable construction process. The methodology is based on the principle of Life Cycle Systems Engineering, and facilitates and structures the introduction of sustainability and stakeholders issues in the design and build process. The method uses manly existing guidelines and standards for Life Cycle Systems Engineering

Hierarchical Reinforcement Learning for Spoken Dialogue Systems

Institute for Communicating and Collaborative SystemsThis thesis focuses on the problem of scalable optimization of dialogue behaviour in speech-based conversational systems using reinforcement learning. Most previous investigations in dialogue strategy learning have proposed flat reinforcement learning methods, which are more suitable for small-scale spoken dialogue systems. This research formulates the problem in terms of Semi-Markov Decision Processes (SMDPs), and proposes two hierarchical reinforcement learning methods to optimize sub-dialogues rather than full dialogues. The first method uses a hierarchy of SMDPs, where every SMDP ignores irrelevant state variables and actions in order to optimize a sub-dialogue. The second method extends the first one by constraining every SMDP in the hierarchy with prior expert knowledge. The latter method proposes a learning algorithm called 'HAM+HSMQ-Learning', which combines two existing algorithms in the literature of hierarchical reinforcement learning. Whilst the first method generates fully-learnt behaviour, the second one generates semi-learnt behaviour. In addition, this research proposes a heuristic dialogue simulation environment for automatic dialogue strategy learning. Experiments were performed on simulated and real environments based on a travel planning spoken dialogue system. Experimental results provided evidence to support the following claims: First, both methods scale well at the cost of near-optimal solutions, resulting in slightly longer dialogues than the optimal solutions. Second, dialogue strategies learnt with coherent user behaviour and conservative recognition error rates can outperform a reasonable hand-coded strategy. Third, semi-learnt dialogue behaviours are a better alternative (because of their higher overall performance) than hand-coded or fully-learnt dialogue behaviours. Last, hierarchical reinforcement learning dialogue agents are feasible and promising for the (semi) automatic design of adaptive behaviours in larger-scale spoken dialogue systems. This research makes the following contributions to spoken dialogue systems which learn their dialogue behaviour. First, the Semi-Markov Decision Process (SMDP) model was proposed to learn spoken dialogue strategies in a scalable way. Second, the concept of 'partially specified dialogue strategies' was proposed for integrating simultaneously hand-coded and learnt spoken dialogue behaviours into a single learning framework. Third, an evaluation with real users of hierarchical reinforcement learning dialogue agents was essential to validate their effectiveness in a realistic environment

Designing Embodied Interactive Software Agents for E-Learning: Principles, Components, and Roles

Embodied interactive software agents are complex autonomous, adaptive, and social software systems with a digital embodiment that enables them to act on and react to other entities (users, objects, and other agents) in their environment through bodily actions, which include the use of verbal and non-verbal communicative behaviors in face-to-face interactions with the user. These agents have been developed for various roles in different application domains, in which they perform tasks that have been assigned to them by their developers or delegated to them by their users or by other agents. In computer-assisted learning, embodied interactive pedagogical software agents have the general task to promote human learning by working with students (and other agents) in computer-based learning environments, among them e-learning platforms based on Internet technologies, such as the Virtual Linguistics Campus (www.linguistics-online.com). In these environments, pedagogical agents provide contextualized, qualified, personalized, and timely assistance, cooperation, instruction, motivation, and services for both individual learners and groups of learners. This thesis develops a comprehensive, multidisciplinary, and user-oriented view of the design of embodied interactive pedagogical software agents, which integrates theoretical and practical insights from various academic and other fields. The research intends to contribute to the scientific understanding of issues, methods, theories, and technologies that are involved in the design, implementation, and evaluation of embodied interactive software agents for different roles in e-learning and other areas. For developers, the thesis provides sixteen basic principles (Added Value, Perceptible Qualities, Balanced Design, Coherence, Consistency, Completeness, Comprehensibility, Individuality, Variability, Communicative Ability, Modularity, Teamwork, Participatory Design, Role Awareness, Cultural Awareness, and Relationship Building) plus a large number of specific guidelines for the design of embodied interactive software agents and their components. Furthermore, it offers critical reviews of theories, concepts, approaches, and technologies from different areas and disciplines that are relevant to agent design. Finally, it discusses three pedagogical agent roles (virtual native speaker, coach, and peer) in the scenario of the linguistic fieldwork classes on the Virtual Linguistics Campus and presents detailed considerations for the design of an agent for one of these roles (the virtual native speaker)

Human Factors in Automated and Robotic Space Systems: Proceedings of a symposium. Part 1

Human factors research likely to produce results applicable to the development of a NASA space station is discussed. The particular sessions covered in Part 1 include: (1) system productivity -- people and machines; (2) expert systems and their use; (3) language and displays for human-computer communication; and (4) computer aided monitoring and decision making. Papers from each subject area are reproduced and the discussions from each area are summarized

Multiple Viewpoints for Tutoring Systems.

This thesis investigates the issue of how a tutoring system, intelligent or otherwise, may be designed to utilise multiple viewpoints on the domain being tutored, and what benefits may accrue from this. The issue was relevant to earlier systems, such as WHY (Stevens et al. 1979) and STEAMER (Hollan et al. 1984). The relevant literature is reviewed, and criteria which must be met by our implementation of viewpoints are established. Viewpoints are conceptualised as pre-defined structures which can be represented in a tutoring system with the potential to increase its effectiveness and adaptability. A formalism is proposed where inferences are drawn from a model by a range of operators. The application of this combination to problems and goals is to be described heuristically. This formulation is then related to the educational philosophy of Cognitive Apprenticeship. The formalism is tested and refined in a protocol analysis study which leads to the definition of three classes of operators. The viewpoint structure is used to produce a detailed formulation of the domain of Prolog debugging for novices, with the goal that students should learn how different bugs may be localised using different viewpoints. Three models of execution are defined, based on those described by Bundy et al. (1985). These are mapped onto a restricted catalogue of bugs by specifying a number of conventions which produce a simplified and consistent domain suited to the needs of novices. VIPER, a tutoring system which can itself accomplish and explain the relevant domain tasks, is described. VIPER is based on a meta-interpreter which produces detailed execution histories which are then analysed. An evaluation of VIPER is reported, with generally favourable results. VIPER is discussed in relation to the research goals, the usefulness of Cognitive Apprenticeship in supporting such a design, and possible future work. This discussion exemplifies the use of established student modeling techniques, the implementation of other viewpoints on Prolog, and the application of the design strategy to other domains. Claims are made in relation to the formulation of viewpoints, the architecture of VIPER, and the relevance of Cognitive Apprenticeship to the use of multiple viewpoints