Over the last ten years there has been a growing interest around text-based chatbots, software applications interacting with humans using natural written language. However, despite the enthusiastic market predictions, ‘conversing’ with this kind of agents seems to raise issues that go beyond their current technological limitations, directly involving the human side of interaction. By adopting a Human-Computer Interaction (HCI) lens, in this article we present a systematic literature review of 83 papers that focus on how users interact with text-based chatbots. We map the relevant themes that are recurrent in the last ten years of research, describing how people experience the chatbot in terms of satisfaction, engagement, and trust, whether and why they accept and use this technology, how they are emotionally involved, what kinds of downsides can be observed in human-chatbot conversations, and how the chatbot is perceived in terms of its humanness. On the basis of these findings, we highlight open issues in current research and propose a number of research opportunities that could be tackled in future years.

The human side of human-chatbot interaction: A systematic literature review of ten years of research on text-based chatbots

Conversational agents in business: A systematic literature review and future research directions

This study investigated the impact of a chatbot-based micro-learning system on students’ learning motivation and performance. A quasi-experiment was conducted with 99 first-year students taking par...

Conversation Technology With Micro-Learning: The Impact of Chatbot-Based Learning on Students’ Learning Motivation and Performance:

Chatbot usage intention analysis: Veterinary consultation

Chatbots have become the go-to platform for users to receive answers to their queries. They are now being used by many businesses too to provide their customers with a virtual assistant to answer their queries. But when it comes to engaging with a user in a dialogue, existing chatbots have several shortcomings, with issues such as failing to provide a meaningful response to the user, offering semantically incorrect information etc. This paper studies the working styles of existing chatbots in generating a response and then identifies their shortcomings from the viewpoint of engaging in a dialogue with a user. It then proposes a domain-specific chatbot named IntelliBot, which is a response-generating dialogue-based chatbot that uses multiple strategies to generate a response. IntelliBot was trained on two datasets, namely the Cornell movie dialogue and a custom-built insurance dataset so it has domain-specific knowledge. The performance of IntelliBot was then validated and compared with three other chatbots from the literature, namely RootyAI, ChatterBot and DeepQA. The results demonstrate IntelliBot’s superiority in engaging with the user and providing a complete answer in the insurance domain.

IntelliBot: A Dialogue-based chatbot for the insurance industry

A chatbot can be defined as a computer program, designed to interact with users using natural language or text in a way that the user thinks he is having dialogue with a human. Most of the chatbots utilise the algorithms of artificial intelligence (AI) in order to generate required response. Earlier chatbots merely created an illusion of intelligence by employing much simpler pattern matching and string processing design techniques for their interaction with users using rule-based and generative-based models. However, with the emergence of new technologies more intelligent systems have emerged using complex knowledge-based models. This paper aims to discuss chatbots classification, their design techniques used in earlier and modern chatbots and how the two main categories of chatbots handle conversation context.

A Survey on Conversational Agents/Chatbots Classification and Design Techniques

Wireless nanosensor networks are envisioned to operate in the THz band, due to the small size of the network devices. The tiny dimensions of the nanosensor devices allow them to be embedded in various objects and devices in our environment to obtain fine-grained data, such as object’s components or its internal status, from hard-to-reach locations in a non-invasive way, and to perform additional in-network processing and thereby enabling a myriad of novel applications in industrial, biomedical, and agricultural settings that cannot be realized with conventional sensor networks. In this work, we propose a crop monitoring/defense application of nanosensors, for detection of any compound released by plants. Based on the detected volatile compounds, the state of the plant in its surrounding can be recognized and chemical nanosensors can release the same natural composites to reinforce the plants defense mechanism in case of insect attack. Our approach centers around the creation of a wireless nanosensor network (WNSN) wherein large number of chemical nanosensor devices, which is connected to a remote center via the Internet to provide remote monitoring/controlling capabilities.

Shafquat Hussain

Papers

A Survey on Conversational Agents/Chatbots Classification and Design Techniques

Environmental Monitoring Intelligent System Using Wireless Nanosensor Networks