These transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as the semantic web, social networks, and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies, such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems. This thirty-second issue presents 5 selected papers in the field of management, economics and computer science.
This two-volume set (LNAI 11683 and LNAI 11684) constitutes the refereed proceedings of the 11th International Conference on Computational Collective Intelligence, ICCCI 2019, held in Hendaye France, in September 2019.The 117 full papers presented were carefully reviewed and selected from 204 submissions. The papers are grouped in topical sections on: knowledge engineering and semantic web; social networks and recommender systems; text processing and information retrieval; data mining methods and applications; computer vision techniques; decision support and control systems; cooperative strategies for decision making and optimization; intelligent modeling and simulation approaches for real world systems; and innovations in intelligent systems.
These transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as the semantic Web, social networks, and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies, such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems. This twenty-eight issue is a special issue with 11 selected papers from the International Conference on Agents and Artificial Intelligence, ICAART 2016 and 2017 editions.
This book presents cutting-edge research on innovative human systems integration and human–machine interaction, with an emphasis on artificial intelligence and automation, as well as computational modeling and simulation. It covers a wide range of applications in the areas of design, construction and operation of products, systems and services, and discusses the human factors in a wide range of settings. Gathering the proceedings of the 3rd International Conference on Intelligent Human Systems Integration (IHSI 2020), held on February 19–21, 2020, in Modena, Italy, the book’s goal is to advance the theory and applications of artificial cognitive systems and improve human-artificial systems collaboration. Special emphasis is placed on automotive design, autonomous vehicles and the applications of artificial intelligence. The book offers a timely survey and source of inspiration for human factors engineers, automotive engineers, IT developers and UX designers who are working to shape the future of automated intelligent systems.
This book discusses various aspects of Industry 4.0 from the perspective of information system evolution. Industry 4.0 refers to a new phase in the industrial revolution that relies heavily on interconnectivity, automation, machine learning, real-time data, the Internet of Things and blockchain technology. The interdisciplinary book addresses a number of topics related to modern information technologies, and presents innovative concepts, methods, models and tools for the development of information systems to support Industry 4.0. Focusing on artificial intelligence, collective knowledge processing and blockchain technology, it appeals to a wide readership, including researchers, students, business managers and professionals, software developers, as well as IT and management specialists.
The two-volume set LNCS 11508 and 11509 constitutes the refereed proceedings of of the 18th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2019, held in Zakopane, Poland, in June 2019. The 122 revised full papers presented were carefully reviewed and selected from 333 submissions. The papers included in the first volume are organized in the following five parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; pattern classification; artificial intelligence in modeling and simulation. The papers included in the second volume are organized in the following five parts: computer vision, image and speech analysis; bioinformatics, biometrics, and medical applications; data mining; various problems of artificial intelligence; agent systems, robotics and control.
This book represents an important voice in the discourse on the adoption of emerging ICT for sustainability. It focuses on how emerging ICT acts as a crucial enabler of sustainability, offering new forward-looking approaches to this field. The book explores how emerging ICT adoption drives sustainability efforts in business and public organizations, promoting ecological, economic, social, cultural, and political sustainability. The book's theoretical discussions, conceptual approaches, empirical studies, diverse perspectives, and views make it a valuable and comprehensive reference work. Appealing to both researchers and practitioners, this book provides significant areas for research and practice related to the contribution of emerging ICT adoption to sustainability. It also suggests vital considerations for programming and building sustainable development-driven emerging ICT adoption. Readers will find answers to important contemporary questions, such as: • What are the concepts, frameworks, models, and approaches to enhance sustainable development through the adoption of emerging ICT? • How does the adoption of emerging ICT influence sustainability? • How can emerging ICT be adopted to enhance sustainability? • What are the current practices and successful cases of emerging ICT adoption for sustainability? • What factors influence emerging ICT adoption to enhance sustainability?
This book features a selection of extended papers presented at the 8th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2021, held in Yokohama, Japan, in January 2021, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2020.* The 14 revised and extended papers presented together with an invited talk were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management and discuss methodological, technical and organizational aspects of artificial intelligence used for knowledge management. *The workshop was held virtually.
Solve design, planning, and control problems using modern AI techniques. Optimization problems are everywhere in daily life. What’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization Algorithms introduces the AI algorithms that can solve these complex and poorly-structured problems. In Optimization Algorithms: AI techniques for design, planning, and control problems you will learn: • The core concepts of search and optimization • Deterministic and stochastic optimization techniques • Graph search algorithms • Trajectory-based optimization algorithms • Evolutionary computing algorithms • Swarm intelligence algorithms • Machine learning methods for search and optimization problems • Efficient trade-offs between search space exploration and exploitation • State-of-the-art Python libraries for search and optimization Inside this comprehensive guide, you’ll find a wide range of optimization methods, from deterministic search algorithms to stochastic derivative-free metaheuristic algorithms and machine learning methods. Don’t worry—there’s no complex mathematical notation. You’ll learn through in-depth case studies that cut through academic complexity to demonstrate how each algorithm works in the real world. Plus, get hands-on experience with practical exercises to optimize and scale the performance of each algorithm. About the technology Every time you call for a rideshare, order food delivery, book a flight, or schedule a hospital appointment, an algorithm works behind the scenes to find the optimal result. Blending modern AI methods with classical search and optimization techniques can deliver incredible results, especially for the messy problems you encounter in the real world. This book shows you how. About the book Optimization Algorithms explains in clear language how optimization algorithms work and what you can do with them. This engaging book goes beyond toy examples, presenting detailed scenarios that use actual industry data and cutting-edge AI techniques. You will learn how to apply modern optimization algorithms to real-world problems like pricing products, matching supply with demand, balancing assembly lines, tuning parameters, coordinating mobile networks, and cracking smart mobility challenges. What's inside • Graph search algorithms • Metaheuristic algorithms • Machine learning methods • State-of-the-art Python libraries for optimization • Efficient trade-offs between search space exploration and exploitation About the reader Requires intermediate Python and machine learning skills. About the author Dr. Alaa Khamis is an AI and smart mobility technical leader at General Motors and a lecturer at the University of Toronto. The technical editor on this book was Frances Buontempo. Table of Contents PART 1 1 Introduction to search and optimization 2 A deeper look at search and optimization 3 Blind search algorithms 4 Informed search algorithms PART 2 5 Simulated annealing 6 Tabu search PART 3 7 Genetic algorithms 8 Genetic algorithm variants PART 4 9 Particle swarm optimization 10 Other swarm intelligence algorithms to explore PART 5 11 Supervised and unsupervised learning 12 Reinforcement learning Appendix A Appendix B Appendix C
This book constitutes the refereed proceedings of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019, held in Turin, Italy, in October 2019. The 25 full papers presented and 25 short papers were carefully reviewed and selected from 112 submissions. The papers presented at the PRIMA 2019 conference focus on the following topics: Logic and Reasoning, Engineering Multi-Agent Systems, Agent-Based Modeling and Simulation, Collaboration and Coordination, Economic Paradigms, Human-Agent Interaction, Decentralized Paradigms, and Application Domains for Multi-Agent Systems.
