Spoken dialogue systems allow users to interact with computer-based applications such as databases and expert systems by using natural spoken language. The origins of spoken dialogue systems can be traced back to Artificial Intelligence research in the 1950s concerned with developing conversational interfaces. However, it is only within the last decade or so, with major advances in speech technology, that large-scale working systems have been developed and, in some cases, introduced into commercial environments. As a result many major telecommunications and software companies have become aware of the potential for spoken dialogue technology to provide solutions in newly developing areas such as computer-telephony integration. Voice portals, which provide a speech-based interface between a telephone user and Web-based services, are the most recent application of spoken dialogue technology. This article describes the main components of the technology---speech recognition, language understanding, dialogue management, communication with an external source such as a database, language generation, speech synthesis---and shows how these component technologies can be integrated into a spoken dialogue system. The article describes in detail the methods that have been adopted in some well-known dialogue systems, explores different system architectures, considers issues of specification, design, and evaluation, reviews some currently available dialogue development toolkits, and outlines prospects for future development.

Conversation interfaces (CIs), or chatbots, are a popular form of intelligent agents that engage humans in taskoriented or informal conversation. In this position paper and demonstration, we argue that chatbots working in dynamic environments, like with sensor data, can not only serve as a promising platform to research issues at the intersection of learning, reasoning, representation and execution for goal-directed autonomy; but also handle non-trivial business applications. We explore the underlying issues in the context of Water Advisor, a preliminary multi-modal conversation system that can access and explain water quality data.

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The aim of this paper is to assess the usability of a chatbot for mental health care within a social enterprise. Chatbots are becoming more prevalent in our daily lives, as we can now use them to book flights, manage savings, and check the weather. Chatbots are increasingly being used in mental health care, with the emergence of "virtual therapists". In this study, the usability of a chatbot named iHelpr has been assessed. iHelpr has been developed to provide guided self-assessment, and tips for the following areas: stress, anxiety, depression, sleep, and self esteem. This study used a questionnaire developed by Chatbottest, and the System Usability Scale to assess the usability of iHelpr. The participants in this study enjoyed interacting with the chatbot, and found it easy to use. However, the study highlighted areas that need major improvements, such as Error Management and Intelligence. A list of recommendations has been developed to improve the usability of the iHelpr chatbot.

Chatbots are becoming increasingly popular as a human-computer interface. The traditional best practices normally applied to User Experience (UX) design cannot easily be applied to chatbots, nor can conventional usability testing techniques guarantee accuracy. WeightMentor is a bespoke self-help motivational tool for weight loss maintenance. This study addresses the following four research questions: How usable is the WeightMentor chatbot, according to conventional usability methods?; To what extend will different conventional usability questionnaires correlate when evaluating chatbot usability?; And how do they correlate to a tailored chatbot usability survey score?; What is the optimum number of users required to identify chatbot usability issues?; How many task repetitions are required for a first-time chatbot users to reach optimum task performance (i.e. efficiency based on task completion times)? This paper describes the procedure for testing the WeightMentor chatbot, assesses correlation between typical usability testing metrics, and suggests that conventional wisdom on participant numbers for identifying usability issues may not apply to chatbots. The study design was a usability study. WeightMentor was tested using a predetermined usability testing protocol, evaluating ease of task completion, unique usability errors and participant opinions on the chatbot (collected using usability questionnaires). WeightMentor usability scores were generally high, and correlation between questionnaires was strong. The optimum number of users for identifying chatbot usability errors was 26, which challenges previous research. Chatbot users reached optimum proficiency in tasks after just one repetition. Usability test outcomes confirm what is already known