Design an Intelligent System to automatically Tutor the Method for Solving Problems

Nowadays, intelligent systems have been applied in many real-word domains. The Intelligent chatbot is an intelligent system, it can interact with the human to tutor how to work some activities. In this work, we design an architecture to build an intelligent chatbot, which can tutor to solve problems, and construct scripts for automatically tutoring. The knowledge base of the intelligent tutoring chatbot is designed by using the requirements of an Intelligent Problem Solver. It is the combination between the knowledge model of relations and operators, and the structures of hint questions and sample problems, which are practical cases. Based on the knowledge base and tutoring scripts, a tutoring engine is designed. The tutoring chatbot plays as an instructor for solving real-world problems. It simulates the working of the instructor to tutor the user for solving problems. By utilizing the knowledge base and reasoning, the architecture of the intelligent chatbot are emerging to apply in the real-world. It is used to build an intelligent chatbot to support the learning of high-school mathematics and a consultant system in public administration. The experimental results show the effectiveness of the proposed method in comparison with the existing systems

Bershca: bringing chatbot into hotel industry in Indonesia

Adopting technology could give competitive advantage and positively impact the hotel’s profitability, thus hotels should keep up with the latest hotel technologies. An important part in the hotel services is the customer service. A problem with the human-to-human customer services today is a long time in answering customers query. On the other hand, nowadays customers need easy and effective services. Thus, a chatbot is required to answer consumers' issues automatically which leads to higher customer satisfaction and a growing profit. Because of the need and there is still an absence of chatbot for hotel industry in Indonesia, this study is conducted. The chatbot for hotel industry in Indonesia, named Bershca, has been successfully developed using artificial intelligence markup language (AIML) to construct the knowledge. Google Flutter is used for the system’s front-end, while Python is used for the back-end of the system. As a text-preprocessing method, Nazief-Adriani Algorithm is implemented in the system’s back-end. The system is evaluated using technology acceptance model (TAM). As a result, 85.7% of the respondents believe that using chatbot would enhance their job performance and 84.33% of the respondents believe that using the technology would be free of effort

TESS: A Multi-intent Parser for Conversational Multi-Agent Systems with Decentralized Natural Language Understanding Models

Chatbots have become one of the main pathways for the delivery of business automation tools. Multi-agent systems offer a framework for designing chatbots at scale, making it easier to support complex conversations that span across multiple domains as well as enabling developers to maintain and expand their capabilities incrementally over time. However, multi-agent systems complicate the natural language understanding (NLU) of user intents, especially when they rely on decentralized NLU models: some utterances (termed single intent) may invoke a single agent while others (termed multi-intent) may explicitly invoke multiple agents. Without correctly parsing multi-intent inputs, decentralized NLU approaches will not achieve high prediction accuracy. In this paper, we propose an efficient parsing and orchestration pipeline algorithm to service multi-intent utterances from the user in the context of a multi-agent system. Our proposed approach achieved comparable performance to competitive deep learning models on three different datasets while being up to 48 times faster.Comment: 16 page

AI gets real at Singapore's Changi Airport (Part 1)

See part 2 on Distilling managerial insights and lessons from AI projects at Singapore’s Changi Airport. See also AI: Augmentation, more so than automation.</p

Mechanisms of Common Ground in Human-Agent Interaction: A Systematic Review of Conversational Agent Research

Human-agent interaction is increasingly influencing our personal and work lives through the proliferation of conversational agents in various domains. As such, these agents combine intuitive natural language interactions by also delivering personalization through artificial intelligence capabilities. However, research on CAs as well as practical failures indicate that CA interaction oftentimes fails miserably. To reduce these failures, this paper introduces the concept of building common ground for more successful human-agent interactions. Based on a systematic review our analysis reveals five mechanisms for achieving common ground: (1) Embodiment, (2) Social Features, (3) Joint Action, (4) Knowledge Base, and (5) Mental Model of Conversational Agents. On this basis, we offer insights into grounding mechanisms and highlight the potentials when considering common ground in different human-agent interaction processes. Consequently, we secure further understanding and deeper insights of possible mechanisms of common ground in human-agent interaction in the future

Theory of Service: The User Perspective

For development of the theory of service, the focus was on the constructs of service. The theory is expanded to encompass the various business models of service and the service encounter including but not limited to guest satisfaction, service quality, service and technology, service system design, and customer relationship management. The theory began with the observed differences in different service settings, the emergence of technology and defining the service experience across multiple sectors. Further consideration is given to the aspects of the service transaction and what constitutes service, where a labor component of a firm’s product has been transferred to the customer. Lastly, self-service and automation are considered in the context of the user experience. Implications for industry application are discussed

An Intent-Based Reasoning System for Automatic Generation of Drone Missions for Public Protection and Disaster Relief

The utilization of drones for search and rescue operations has become more prevalent over the years. Drones can provide an aerial perspective which can aid first responders in gaining an overview of a situation. Autonomous drones can automate search and rescue operations by removing the human pilot, which can increase efficiency and lower costs. The increased development of machine learning models and techniques has paved the way for intent-based reasoning systems that can understand users' intent. This can allow users to control autonomous drones by expressing their intent. Which can be utilized for search and rescue operations. However, machine learning models require vast computational power and data storage. In addition, autonomous drones have high-performance requirements. The development of 5G can provide the infrastructure required to meet the stringent performance requirements of machine learning models and autonomous drones. By leveraging the advanced features of 5G, such as network slicing, high-speed communication, and low latency, it provides the infrastructure that supports the use of machine learning models in coordination with drones. This thesis proposes a system prototype that can generate drone missions based on user intent which can be used for rescue operations. The system utilizes a large language model and automatic speech recognition model to capture the intent of the user and generate drone missions that integrate with a 4G-enabled drone. The evaluation of the system reveals that the system can reliably capture the user's intent with simple commands, but struggles with more complex commands. The prototype demonstrates that intent-based reasoning systems for controlling autonomous drones using 5G technology can aid first responders during PPDR missions