Computational Collective Intelligence

This book constitutes the refereed proceedings of the 15th International Conference on Computational Collective Intelligence, ICCCI 2023, held in Budapest, Hungary, during September 27–29, 2023. The 63 full papers included in this book were carefully reviewed and selected from 218 submissions. They are organized in topical sections as follows: collective intelligence and collective decision-making; deep learning techniques; natural language processing; data mining and machine learning; social networks and intelligent systems; cybersecurity, blockchain technology and Internet of Things; cooperative strategies for decision making and optimization; computational intelligence for digital content understanding; knowledge engineering and application for Industry 4.0; computational intelligence in medical applications; and ensemble models and data fusion.

Introducing a groundbreaking approach to understanding, measuring, and applying Collective Intelligence Does Collective Intelligence (CI) exist and, if so, how can it be characterized, quantified, and harnessed? Questions such as these continue to be hotly debated within both the scientific and philosophical communities. Yet few researchers working in the fields of artificial intelligence or distributed computing doubt CI's enormous potential value to the future of computing. Unfortunately, for lack of a rigorous, formal theory of Collective Intelligence, most attempts to analyze CI systems have been disappointing, at best. In Computational Collective Intelligence, Professor Tadeusz Szuba does much to rectify that situation by developing, for the first time, both a formal definition of CI and practical guidelines for its assessment and applications. Working from the ground up, Dr. Szuba begins with a stimulating and insightful discussion of the types of intelligence-including individual, artificial, and collective-into which he brings ideas from AI, information theory, and distributed computing, as well as psychology, sociology, animal behavior, cognitive science, and other relevant disciplines. He tackles the problem of computational models for simulating and measuring CI. He explores all theoretically feasible models of CI computations and presents a groundbreaking, nondeterministic approach using the Random PROLOG Processor (RPP) as a CI modeling and evaluation tool. He then introduces the Collective Intelligence Quotient (IQS) and develops clear-cut guidelines for measuring it. In the final chapters, he lays the foundation for a dynamic new discipline, Collective Intelligence Engineering (CIE), and considers its potential applications as an organizational restructuring tool.

The book consists of 35 extended chapters which have been selected and invited from the submissions to the 4th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2012) held on November 28-30, 2012 in Ho Chi Minh City, Vietnam. The book is organized into six parts, which are semantic web and ontologies, social networks and e-learning, agent and multiagent systems, data mining methods and applications, soft computing, and optimization and control, respectively. All chapters in the book discuss theoretical and practical issues connected with computational collective intelligence and related technologies. The editors hope that the book can be useful for graduate and Ph.D. students in Computer Science, in particular participants in courses on Soft Computing, Multiagent Systems, and Data Mining. This book can be also useful for researchers working on the concept of computational collective intelligence in artificial populations. It is the hope of the editors that readers of this volume can find many inspiring ideas and use them to create new cases of intelligent collectives. Many such challenges are suggested by particular approaches and models presented in individual chapters of this book. The editors hope that readers of this volume can find many inspiring ideas and influential practical examples and use them in their future work.

This book consists of 20 chapters in which the authors deal with different theoretical and practical aspects of new trends in Collective Computational Intelligence techniques. Computational Collective Intelligence methods and algorithms are one the current trending research topics from areas related to Artificial Intelligence, Soft Computing or Data Mining among others. Computational Collective Intelligence is a rapidly growing field that is most often understood as an AI sub-field dealing with soft computing methods which enable making group decisions and processing knowledge among autonomous units acting in distributed environments. Web-based Systems, Social Networks, and Multi-Agent Systems very often need these tools for working out consistent knowledge states, resolving conflicts and making decisions. The chapters included in this volume cover a selection of topics and new trends in several domains related to Collective Computational Intelligence: Language and Knowledge Processing, Data Mining Methods and Applications, Computer Vision, and Intelligent Computational Methods. This book will be useful for graduate and PhD students in computer science as well as for mature academics, researchers and practitioners interested in the methods and applications of collective computational intelligence in order to create new intelligent systems.

The book consists of 19 extended and revised chapters based on original works presented during a poster session organized within the 5th International Conference on Computational Collective Intelligence that was held between 11 and 13 of September 2013 in Craiova, Romania. The book is divided into three parts. The first part is titled “Agents and Multi-Agent Systems” and consists of 8 chapters that concentrate on many problems related to agent and multi-agent systems, including: formal models, agent autonomy, emergent properties, agent programming, agent-based simulation and planning. The second part of the book is titled “Intelligent Computational Methods” and consists of 6 chapters. The authors present applications of various intelligent computational methods like neural networks, mathematical optimization and multistage decision processes in areas like cooperation, character recognition, wireless networks, transport, and metal structures. The third part of the book is titled “Language and Knowledge Processing Systems”, and consists of 5 papers devoted to processing methods for knowledge and language information in various applications, including: language identification, corpus comparison, opinion classification, group decision making, and rule bases.

This volume constitutes the refereed proceedings of the 12th International Conference on Computational Collective Intelligence, ICCCI 2020, held in Da Nang, Vietnam, in November 2020.* The 70 full papers presented were carefully reviewed and selected from 314 submissions. The papers are grouped in topical sections on: knowledge engineering and semantic web; social networks and recommender systems; collective decision-making; applications of collective intelligence; data mining methods and applications; machine learning methods; deep learning and applications for industry 4.0; computer vision techniques; biosensors and biometric techniques; innovations in intelligent systems; natural language processing; low resource languages processing; computational collective intelligence and natural language processing; computational intelligence for multimedia understanding; and intelligent processing of multimedia in web systems. *The conference was held virtually due to the COVID-19 pandemic.

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.

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect