USING TAGS IN AN AIML-BASED CHATTERBOT TO IMPROVE ITS KNOWLEDGE

BackgroundSocial robots are anthropomorphised platforms developed to interact with humans, using natural language, offering an accessible and intuitive interface suited to diverse cognitive abilities. Social robots can be used to support people with dementia (PwD) and carers in their homes managing medication, hydration, appointments, and evaluating mood, wellbeing, and potentially cognitive decline. Such robots have potential to reduce care burden and prolong independent living, yet translation into PwD use remains insignificant.MethodWe have developed two social robots ‐ a conversational robot and a digital social robot for mobile devices capable of communicating through natural language (powered by Amazon Alexa) and facial expressions that ask PwD daily questions about their health and wellbeing and also provide digital assistant functionality. We record data comprising of PwD’s responses to daily questions, audio speech and text of conversations with Alexa to automatically monitor their health and wellbeing using machine learning. We followed user‐centric development processes by conducting focus groups with 13 carers, 2 PwD and 5 clinicians to iterate the design. We are testing social robot with 3 PwD in their homes for ten weeks.ResultWe received positive feedback on social robot from focus group participants. Ease of use, low maintenance, accessibility, assistance with medication, supporting with health and wellbeing were identified as the key opportunities for social robots. Based on responses to a daily questionnaire, our robots generate a report detailing PwD wellbeing that is automatically sent via email to family members or carers. This information is also stored systematically in a database that can help clinicians monitor their patients remotely. We use natural language processing to analyse conversations and identify topics of interest to PwD such that robot behaviour could be adapted. We process speech using signal processing and machine learning classifiers to identify emotions and mood.ConclusionAffective social robots with voice and facial expressions have a great potential for enhancing dementia care at home. Combining responses to daily questions, topics of conversations and emotions from speech analysis can help assess daily status of PwD automatically and monitor their health and well‐being over time remotely.

Simulation modeling provides powerful decision support capabilities for production planning in manufacturing systems, yet widespread adoption remains limited by significant barriers in model development and maintenance. Traditional simulation approaches require substantial expertise, time investment, and financial resources, creating accessibility challenges particularly for small and medium enterprises. Current research on automatic simulation generation overlooks the significant potential that generative AI can offer in bridging the gap between natural language expression and executable model creation. This paper presents a novel methodology integrating large language models (LLMs) with automatic simulation model generation (ASMG) to enable direct generation of executable simulation models from natural language conversations. Unlike existing ASMG approaches that require formal specifications or simulation knowledge, our framework reduces programming requirements by allowing users to create functional models through conversational interaction. Our approach leverages natural language processing capabilities to transform user requests into executable simulation models without requiring specialized programming knowledge, enhancing human–machine interaction in production planning environments. The framework implements a four-step process: user intent expression through natural language interfaces, knowledge extraction using structured templates, simulation model construction via object-oriented data-driven components, and simulation execution with comprehensive result analysis for resource utilization, bottleneck identification, and production scheduling optimization. Performance testing demonstrates successful model generation across 18 supported action types with response times ranging from 8 s for simple operations to 5 min for complete manufacturing system creation. By enabling direct conversion of natural language requests into functional simulation models, this LLM-enhanced ASMG approach represents a step toward expanding access to simulation technology for non-expert users while maintaining the analytical power needed for complex manufacturing decision support and production control.

This paper addresses the following problem of information extraction using an AI-powered chatbot. An AI chatbot processes a natural language conversation with a human user using semantic search and human-chatbot interaction to extract the most relevant and contextually appropriate information from available databases. During a natural language conversation between a human and an AI, the human user can modify and refine queries until he/she is satisfied with the chatbot’s output. The reliability of an AI chatbot is defined as its ability to understand user queries and provide correct answers; in the current study, it is measured by the frequency (probability) of correct answers. The search efficiency of a chatbot indicates how accurate and relevant the information returned by the chatbot is; in this work, it is measured by the satisfaction level that the human user receives for a correct answer. We uncover a counterintuitive relationship between AI chatbot reliability and search efficiency: we demonstrate that, under fairly general conditions, a less reliable AI chatbot can have a higher expected search efficiency than a more reliable chatbot. This phenomenon aligns with a family of “more-for-less” paradoxes observed in various complex systems. Finally, we discuss the underlying mechanism of this paradox.

Abstract: In an era marked by the convergence of personal and technical facets of life, the need for a versatile and intelligent companion becomes increasingly apparent. This project introduces the development of a novel Chatbot, aptly named the "Personal and Technical Companion," designed to seamlessly integrate into users' lives by offering personalized support and technical assistance. The Chatbot employs state-of-the-art Natural Language Processing (NLP) techniques to engage users in natural language conversations, providing a unique blend of personal and technical guidance. The system architecture encompasses a user-friendly interface, a robust NLP engine for intent recognition, a dynamic backend server handling logic and personalization, and integration with external services to enrich the user experience. Simultaneously, it serves as a technical companion by offering code assistance, troubleshooting guidance, and recommending relevant learning resources. Implementation leverages cutting-edge technologies, including Python for backend logic, Flask for the server, TensorFlow for NLP processing, and HTML/CSS for the user interface.

A model-based solution for automated (Re-)engineering of task-oriented chatbots

Computer storage and retrieval of generic chemical structures in patents. Algorithmic generation of fragment descriptors for generic structure screening : Stephen M. Welford, Michael F. Lynch and John M. Barnard (Department of Information Studies, University of Sheffield, U.K.) J. Chem. Inf. Comput. Sci., 1984 No. 24, pp. 57–66

The evolving landscape of healthcare has witnessed remarkable transformations owing to the integration of Artificial Intelligence (AI) and chatbot technology. This paper presents the development of an AI-powered dental consultation chatbot aimed at revolutionizing dental healthcare accessibility and awareness, particularly in the context of Malaysia. The chatbot serves as a valuable resource for dental self-diagnosis, information dissemination, and remote consultations, reducing the need for in-person visits to dental clinics, especially considering the COVID-19 pandemic. The adoption of chatbots in healthcare has gained momentum, driven by their ability to engage in natural language conversations, disseminate reliable information, and provide personalized assistance. Chatbots have emerged as essential tools in public health management, offering services ranging from disease understanding and information provision to counselling and facilitating doctor-patient communication. In the specific domain of dental healthcare, chatbots have the potential to alleviate challenges associated with iatrophobia (fear of disclosing dental problems) and the need for professional dental consultations. The objective of this work is to develop a user-friendly, AI-powered dental consultation chatbot accessible online. This chatbot allows users to engage in real-life conversations with the machine, conducting short self-diagnosis of their oral health conditions. Users can seek information about their dental issues, understand symptoms, and gain insights into their dental health before or after visiting a dentist. Furthermore, the chatbot system presented in this study holds the potential to ease the workload of dental practitioners by offering an alternative method of consultation. Modern digital dental technologies are increasingly focused on diagnosis and classification tasks, but few have explored the realm of dental consultation chatbots for public use. This project bridges this gap and extends the utility of AI-powered chatbots to the field of dental healthcare. This AI-powered dental consultation chatbot not only addresses the evolving healthcare needs of the post-pandemic era but also contributes to advancing dental healthcare accessibility and awareness among the population.

Misconfigurations in enterprise IT systems remain a leading cause of service disruptions and vulnerabilities. In this paper, we present a self-adaptive AI agent that interactively collects diagnostic symptoms through natural language conversations and collaborates with an automated diagnosis system to identify root causes. The AI agent holds a dialog with the user to clarify the user complaint and then translates it into structured diagnostic tickets by mapping symptoms to relevant configuration variables (CVs) and assigning initial confidence scores. These tickets guide ConfExp, a confidence-driven diagnostic framework, which refines hypotheses through system-level test execution. The combined system significantly improves the diagnosis process. In evaluations across 75 fault scenarios in emulated enterprise environments, the system correctly identified misconfigured components in over 93% of cases while reducing the number of tests required by up to 47% compared to using ConfExp alone. This demonstrates the e ectiveness of combining a large language model (LLM)-driven symptom elicitation with confidence-guided test execution for scalable, automated troubleshooting.

Operation ARA is a serious game that teaches scientific inquiry using natural language conversations. Within the context of the game, students completed up to two distinct training modules that teach either didactic or applied conceptual information about research methodology (e.g., validity of dependent variables, need for control groups). An experiment using a 4-condition between-subjects pretest-interaction-posttest design was conducted in which 81 undergraduate college students interacted with varying modules of Operation ARA. The four conditions were designed to test the impact of the two distinct modules on different types of learning measured by multiple-choice, short answer, and case-based assessment questions. Results revealed significant differences on training condition and learning gains on two of the three types of questions.

The project titled "Revolutionising Learning with AI-Powered Chatbots and NLP Techniques" aims to leverage cutting-edge technology to enhance the learning experience. It features an AI-based chatbot powered by the ChatGPT API, providing learners with a natural and interactive way to seek information and assistance. This chatbot not only responds to text queries but also incorporates voice-to-text and text-to-voice capabilities, akin to Google Assistant, ensuring accessibility for a wider range of users. Furthermore, the project integrates image text recognition, similar to Google Lens, allowing users to extract information from images. For an added dimension, the system enables users to send text commands to the chatbot and receive AI-generated images, making learning more engaging and informative. By combining these features, the project provides a comprehensive and innovative approach to learning by making information accessible through multiple modalities, thus catering to various learning styles and preferences. Whether it's through natural language conversations, voice commands, or image-based queries, this system promises to transform the way users interact with educational content and empower them to explore, learn, and discover in a manner that best suits their individual needs and preferences. Key Words: AI, Chatbots, NLP, ML, Voice-to-Text, Image Text Recognition.

Evaluation of bibliographic data base operation in an SDI system: Kazimierz Kowalski and Aleksander Zgrzywa (Main Library and Scientific Information Centre, Technical University of Wroclaw, Poland). J. Inf. Sci., 1984, Vol. 8, pp. 57–61

ChatGPT is a powerful tool that assists software engineers in identifying and rectifying errors in code. One of its primary advantages is its ability to engage in natural language conversation with humans, which allows it to collaborate more closely with engineers in improving and optimizing the code. However, despite its potential advantages, software developers do not always utilize ChatGPT as a tool for bug fixing. In this study, we aim to examine the factors that influence the adoption of ChatGPT for bug fixing among professional software developers, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) theory. To accomplish this, we conducted 50 semi-structured interviews with professional software developers and other stakeholders. Our findings indicate that the performance expectancy and effort expectancy of professional software developers, as well as social influence, facilitating conditions, data security, and trust are the key factors of adoption. These findings suggest that understanding these factors can be critical in promoting the adoption and use of ChatGPT in the software development industry.

This book explores a virtually untapped, yet fascinating research area: television dialogue. It reports on a study comparing the language of the American situation comedy Friends to natural conversation. Transcripts of the television show and the American English conversation portion of the Longman Grammar Corpus provide the data for this corpus-based investigation, which combines Douglas Biber’s multidimensional methodology with a frequency-based analysis of close to 100 linguistic features. As a natural offshoot of the research design, this study offers a comprehensive description of the most common linguistic features characterizing natural conversation. Illustrated with numerous dialogue extracts from Friends and conversation, topics such as vague, emotional, and informal language are discussed. This book will be an important resource not only for researchers and students specializing in discourse analysis, register variation, and corpus linguistics, but also anyone interested in conversational language and television dialogue.

Software Test Path Identification for Virtual Assistants Based on Natural Language Conversation Flow Chart

This book investigates the intricate interplay between language and food in natural conversations among people eating and talking about food in English, Japanese, Wolof, Eegimaa, Danish, German, Arabic, Persian, and Turkish. It is a socio-cultural/ linguistic study of how adults/ children organize their language and bodies to (1) accomplish rituals and performances of commensality (eating together) and food-related actions, (2) taste, describe, identify and assess food, and influence others’ preferences, (3) create and reinforce individual and group identities through past experiences and stories about food, and (4) socialize one another to food practices, affect, taste, gender and health norms. Using approaches from linguistics, conversation analysis, ethnography, discursive psychology, and linguistic anthropology, this book elucidates the dynamic verbal and nonverbal co-construction of food practices, assessments, categories, and identities in conversations over and about food, and contributes to research on contextualized social, cultural, and cognitive activity, language and food, and cross-cultural understanding.