Chatbot

About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.

Towards Kant Machines

Abstract: For some years now, ethics no longer only means human ethics. The young discipline of machine ethics researches the morality of semi-autonomous and autonomous systems like self-driving cars, robots and drones. Interactive software systems such as chatbots are also relevant. In 2013, the School of Business at the University of Applied Sciences and Arts Northwestern Switzerland FHNW implemented a prototype of the GOODBOT, which is a novelty chatbot and a simple moral machine. One of its meta-rules was that it should not lie unless not lying would hurt the user. In a follow-up project in 2016, the LIEBOT was developed, a kind of Munchausen machine. This article describes the back-ground and the foundations of this project and lists the chatbot’s strategies of lying. Then it discusses how Munchausen machines as immoral machines can contribute to the construction and optimization of moral machines, for example Kant machines, which prefer the truth. The LIEBOT serves as a contribution to machine ethics as well as a critical review of electronic language-based systems and services.

An Architectural Design of ScratchThAI: A conversational agent for Computational Thinking Development using Scratch

Abstract: Scratch is a visual, block-based programming language, adopted as a computational thinking development tool in elementary education among many countries. Thailand has also recently included Scratch as part of the computing science course in its basic education. However, Thailand is facing a shortage of ICT teachers who are skillful in Scratch programming, especially in small provincial schools. This research aims to overcome the shortage by developing ScratchThAI, a Scratch tutorial chatbot. It is designed to assist young learners directly through a messaging platform. By giving supports through a textual conversation, more relevant advice, knowledge, and resources could be provided precisely. Different levels of each computational thinking concept are extracted and evaluated by the designed assessment algorithm. Extra predefined exercises are assigned based on the analyzed learner's strengths and weaknesses in order to actively improving the learner's understanding. Moreover, gamification is incorporated to engage and motivate young learners in computational thinking development.

Survey of Textbased Chatbot in Perspective of Recent Technologies

Abstract: Chatbots are computer programs capable to carry a conversation with human. They can be seen as an artificial agent designed to serve the purpose of conversation with the end user. Chatbots are gaining popularity especially in business and health sector as they have the potential to automate service and reduce human efforts. Widespread use of Apps, maturation of Artificial Intelligence (AI) technologies and integration of Natural Language Processing (NLP) fuels up the growth of chatbot. In this paper, we present different models of chatbots along with an architectural overview of computationally intelligent chatbot in context of recent technologies. In the three layer architecture, we have given insights of how the NLP, Natural Language Understanding (NLU) and Decision engine work together with Knowledge Base to achieve AI using Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM). In addition, we also discuss different chatbot platforms and development frameworks of recent times. Our core emphasis is on analysis of recent development approaches of textbased conversational systems. We identify few challenges in intelligent chatbot development that may be helpful for future research works.

Investigating the user experience of customer service chatbot interaction: a framework for qualitative analysis of chatbot dialogues

Abstract: The uptake of chatbots for customer service depends on the user experience. For such chatbots, user experience in particular concerns whether the user is provided relevant answers to their queries and the chatbot interaction brings them closer to resolving their problem. Dialogue data from interactions between users and chatbots represents a potentially valuable source of insight into user experience. However, there is a need for knowledge of how to make use of these data. Motivated by this, we present a framework for qualitative analysis of chatbot dialogues in the customer service domain. The framework has been developed across several studies involving two chatbots for customer service, in collaboration with the chatbot hosts. We present the framework and illustrate its application with insights from three case examples. Through the case findings, we show how the framework may provide insight into key drivers of user experience, including response relevance and dialogue helpfulness (Case 1), insight to drive chatbot improvement in practice (Case 2), and insight of theoretical and practical relevance for understanding chatbot user types and interaction patterns (Case 3). On the basis of the findings, we discuss the strengths and limitations of the framework, its theoretical and practical implications, and directions for future work.

When There is No Progress with a Task-Oriented Chatbot: A Conversation Analysis

Abstract: Task-oriented chatbots are increasingly prevalent in our daily life. Research effort has been devoted to advancing our understanding of users' interaction with conversational agents, including conversation breakdowns. However, most research attempts were limited to obversions from a relatively short duration of user interaction with chatbots, where users were aware of being studied. In this study, we conducted a conversation analysis on a three-month conversation log of users conversing with a chatbot of a banking institution. The log consisted of 1,837 users' conversations with this chatbot with 19,449 message exchanges. From this analysis, we show that users more often failed to make a progress in a conversation when they requested information than when they provided information. Furthermore, we uncovered five kinds of intention gaps unexpected to the chatbot, and five major behaviors users adopted to cope with non-progress.