Chatbots for Modelling, Modelling of Chatbots

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-03-202

Model-Driven Chatbot Development

Esta versión del artículo ha sido aceptada para su publicación, después de la revisión por pares (cuando corresponda) y está sujeta a los términos de uso de AM de Springer Nature, pero no es la Versión de Registro y no refleja mejoras posteriores a la aceptación, ni ninguna corrección. La versión del registro está disponible en línea en: https://doi.org/10.1007/978-3-030-62522-1_15Chatbots are software services accessed via conversation in natural language. They are increasingly used to help in all kinds of procedures like booking flights, querying visa information or assigning tasks to developers. They can be embedded in webs and social networks, and be used from mobile devices without installing dedicated apps. While many frameworks and platforms have emerged for their development, identifying the most appropriate one for building a particular chatbot requires a high investment of time. Moreover, some of them are closed – resulting in customer lock-in – or require deep technical knowledge. To tackle these issues, we propose a model-driven engineering approach to chatbot development. It comprises a neutral meta-model and a domainspecific language (DSL) for chatbot description; code generators and parsers for several chatbot platforms; and a platform recommender. Our approach supports forward and reverse engineering, and model-based analysis. We demonstrate its feasibility presenting a prototype tool and an evaluation based on migrating third party Dialogflow bots to RasaWork funded by the Spanish Ministry of Science (RTI2018095255-B-I00) and the R&D programme of Madrid (P2018/TCS-4314

Factors Influencing Artificial Intelligence Conversational Agents Usage in the E-commerce Field: A Systematic Literature Review

Artificial intelligence conversational agents have become an important strategy for business, both as an online shopping application and as a customer support solution, where they provide interactive communication for online customers. To ensure the effective usage and successful implementation of the conversational agents, the factors influencing customers\u27 attitudes and acceptance towards conversational agents need to be explored. This paper presents a systematic literature review of conversational agents in the field of e-commerce to identify the variables that influence conversational agents\u27 usage and to present the state-of-the-art in this research area. Twenty-four relevant papers are reviewed, and many significant factors are identified that positively influence customers\u27 acceptance, satisfaction, and trust towards conversational agents’ technology

Automated Synthesis of Chatbots for Configuring Software Product Lines

Software product lines are a method for creating a family of products that share a typical managed set of features, satisfy the precise needs of a selected domain, and provide an improved quality of software systems by systematically reusing software artefacts at reduced cost and time. A feature model represents the space of all possible and allowed configurations of all products in an SPL. Various predefined feature combinations enable the product to be personalized based on specific user requirements. However, because some features are interdependent and the feature models may have many options, users must understand the implications of selecting the correct feature combinations for the product derivation. Chatbot support can address this challenge by guiding the user through a suitable set of features for the product configuration process. Users can interact with a chatbot using natural language in a familiar environment like Telegram, Slack, or Facebook. In this work, we propose chatbots in the configuration of software product lines based on feature models and present SPLBOT, an approach for SPLs chatbot generators. The methodology relies on Eclipse, FeatureIDE, and CONGA (for Dialogflow chatbot generation). Furthermore, we present an evaluation of our approach’s effectiveness and scalability using three practical examples

Chatbots at Digital Workplaces – A Grounded-Theory Approach for Surveying Application Areas and Objectives

Background: Chatbots are currently on the rise as more and more researchers tackle this topic from different perspectives. Simultaneously, workplaces and ways of working are increasingly changing in the context of digitalization. However, despite the promised benefits, the changes still show problems that should be tackled more purposefully by chatbots. Application areas and underlying objectives of a chatbot application at digital workplaces especially have not been researched yet. Method: To solve the existing problems and close the research gap, we did a qualitative empirical study based on the grounded-theory process. Therefore, we interviewed 29 experts in a cross-section of different industry sectors and sizes. The experts work in the information systems domain or have profound knowledge of (future) workplace design, especially regarding chatbots. Results: We identified three fundamental usage scenarios of chatbots in seven possible application areas. As a result of this, we found both divisional and cross-divisional application areas at workplaces. Furthermore, we detected fifteen underlying objectives of a chatbot operation, which can be categorized from direct over mid-level to indirect ones. We show dependencies between them, as well. Conclusions: Our results prove the applicability of chatbots in workplace settings. The chatbot operation seems especially fruitful in the support or the self-service domain, where it provides information, carries out processes, or captures process-related data. Additionally, automation, workload reduction, and cost reduction are the fundamental objectives of chatbots in workplace scenarios. With this study, we contribute to the scientific knowledge base by providing knowledge from practice for future research approaches and closing the outlined research gap. Available at: https://aisel.aisnet.org/pajais/vol12/iss2/3

A Bibliometric Survey of Fashion Analysis using Artificial Intelligence

In the 21st century, clothing fashion has become an inevitable part of every individual human as it is considered a way to express their personality to the outside world. Currently the traditional fashion business models are experiencing a paradigm shift from being an experience-based business strategy implementation to a data driven intelligent business improvisation. Artificial Intelligence is acting as a catalyst to achieve the infusion of data intelligence into the fashion industry which aims at fostering all the business brackets such as supply chain management, trend analysis, fashion recommendation, sales forecasting, digitized shopping experience etc. The field of “Fashion AI\u27\u27 is still under research progress because the fashion data is a multifaceted entity which is available in any of the forms like an image, video, text and numerical values. Therefore, it becomes a challenging research arena. There is a paucity of a common study which can provide a bird’s eye view about the research efforts and directions. In this paper, the authors represent a bibliometric survey of the AI based fashion analysis domain based on the Scopus database. The study was conducted by retrieving 581 Scopus research papers published from 1975-2020 and analysed to find out critical insights such as publication volume, co-authorship networks, citation analysis, and demographic research distribution. The study revealed that significant contribution is made via concept propositions in conferences and some papers published in the journal. However, there is a scope of lots of research work in the direction of improving fashion industry with AI techniques

A Bibliometric Survey of Fashion Analysis using Artificial Intelligence

In the 21st century, clothing fashion has become an inevitable part of every individual human as it is considered a way to express their personality to the outside world. Currently the traditional fashion business models are experiencing a paradigm shift from being an experience-based business strategy implementation to a data driven intelligent business improvisation. Artificial Intelligence is acting as a catalyst to achieve the infusion of data intelligence into the fashion industry which aims at fostering all the business brackets such as supply chain management, trend analysis, fashion recommendation, sales forecasting, digitized shopping experience etc. The field of “Fashion AI\u27\u27 is still under research progress because the fashion data is a multifaceted entity which is available in any of the forms like an image, video, text and numerical values. Therefore, it becomes a challenging research arena. There is a paucity of a common study which can provide a bird’s eye view about the research efforts and directions. In this paper, the authors represent a bibliometric survey of the AI based fashion analysis domain based on the Scopus database. The study was conducted by retrieving 581 Scopus research papers published from 1975-2020 and analysed to find out critical insights such as publication volume, co-authorship networks, citation analysis, and demographic research distribution. The study revealed that significant contribution is made via concept propositions in conferences and some papers published in the journal. However, there is a scope of lots of research work in the direction of improving fashion industry with AI techniques