This literature review presents the History, Technology, and Applications of Natural Dialog Systems or simply chatbots. It aims to organize critical information that is a necessary background for further research activity in the field of chatbots. More specifically, while giving the historical evolution, from the generative idea to the present day, we point out possible weaknesses of each stage. After we present a complete categorization system, we analyze the two essential implementation technologies, namely, the pattern matching approach and machine learning. Moreover, we compose a general architectural design that gathers critical details, and we highlight crucial issues to take into account before system design. Furthermore, we present chatbots applications and industrial use cases while we point out the risks of using chatbots and suggest ways to mitigate them. Finally, we conclude by stating our view regarding the direction of technology so that chatbots will become really smart.

Chatbots: History, technology, and applications

The use of chatbots evolved rapidly in numerous fields in recent years, including Marketing, Supporting Systems, Education, Health Care, Cultural Heritage, and Entertainment. In this paper, we first present a historical overview of the evolution of the international community’s interest in chatbots. Next, we discuss the motivations that drive the use of chatbots, and we clarify chatbots’ usefulness in a variety of areas. Moreover, we highlight the impact of social stereotypes on chatbots design. After clarifying necessary technological concepts, we move on to a chatbot classification based on various criteria, such as the area of knowledge they refer to, the need they serve and others. Furthermore, we present the general architecture of modern chatbots while also mentioning the main platforms for their creation. Our engagement with the subject so far, reassures us of the prospects of chatbots and encourages us to study them in greater extent and depth.

/pdf/an-overview-of-chatbot-technology-4xl0hv249k.pdf

An Overview of Chatbot Technology

I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents

Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

A Survey on Conversational Recommender Systems

In the last few years there has been a fast growing up of the use of Chatbots in various fields, such as Health Care, Marketing, Educational, Supporting Systems, Cultural Heritage, Entertainment and many others. This paper presents the realization of a prototype of a Chatbot in educational domain: the purpose has focused on the design of the specific architecture, model to manage communication and furnish the right answers to the student. For this aim, it has been realized a system that can detect the questions and thanks to the use of natural language processing techniques and the ontologies of domain, gives the answers to student. Finally, after the implementation of the designed model, experimental campaign was conducted in order to demonstrate its utility.

Chatbot: An Education Support System for Student

Nowadays the use of Chatbots is very popular in a large scale of applications especially in systems that provide an intelligence support to the user. In fact, to speed up the assistance, in many cases, these systems are equipped with Chatbots that can interpret the user questions and provide the right answers, in a fast and correct way. This paper presents the realization of a prototype of a Chatbot in educational domain: it has been developed a system to provide support to university students on some courses. The initial purpose has focused on the design of the specific architecture, model to manage communication and furnish the right answers to the student. For this aim, it has been realized a system that can detect the questions and thanks to the use of natural language processing techniques and the ontologies of domain, gives the answers to student. Finally, after the implementation of the designed model, experimental campaign was conducted in order to demonstrate its enforceability and efficiency.

Chatbot for e-learning: A case of study

In recent years, the growing number of devices connected to the internet has increased significantly. These devices can interact with the external environment and with human beings through a wide range of sensors that, perceiving reality through the digitization of some parameters of interest, can provide an enormous amount of data. All this data is then shared on the network with other devices and with different applications and infrastructures. This dynamic and ever-changing world underlies the Internet of Things (IoT) paradigm. To date, countless applications based on IoT have been developed; think of Smart Cities, smart roads, and smart industries. This article analyzes the current architectures, technologies, protocols, and applications that characterize the paradigm.

https://www.mdpi.com/2078-2489/12/2/87/pdf

Internet of things: A general overview between architectures, protocols and applications

CHAT-Bot: A cultural heritage aware teller-bot for supporting touristic experiences

Our cities are significantly changing their structure and organization. These changes have given rise to the need to introduce new services able to rationalize the activities present in such a complex context. Within this scenario, one of the most important services is related to transport management. A proper transport system management can significantly improve the overall quality of life of citizens, in terms of improving air quality, reducing road traffic and ensuring the public transport schedule. In addition to the traditional services and urban traffic management approaches, a significant contribution may come from the adoption of all those IT systems, which are part of the so-called Internet of things. According to this paradigm, it is possible to design an added value and pervasive services in order to assist the users involved in the system. Although this approach could be considered interesting and promising, it is necessary to introduce methodologies able to manage data coming from several heterogeneous sensors in order to process and propose coherent information. In this paper, we propose the using of three graphic approaches able to process the information coming from various sources in order to manage urban transport systems. The three models of representation, on which to conduct inference processes are context dimension tree, ontology and Bayes network. These three approaches allow the creation of inference processes, which represent the basis of value-added services to be offered to several users. The aim of this paper is to present a service that through a multilevel approach, which takes advantage of three models of graphic representation, is able to analyse data from various sensors in an urban area in order to predict the bicycle-sharing public service usage in the city of London. Through the intersection and analysis of data from cameras, weather and transport sensors, it will be possible to establish in which condition there will be an increase or decrease of bicycle rental in order to manage the service. The results obtained on data collected in real scenarios are very satisfying.

Marco Lombardi

Papers

Chatbot: An Education Support System for Student

Chatbot for e-learning: A case of study

Internet of things: A general overview between architectures, protocols and applications

CHAT-Bot: A cultural heritage aware teller-bot for supporting touristic experiences

A Multilevel Graph Approach for Predicting Bicycle Usage in London Area.