Costin Bădică

Djamal Benslimane

Jan Treur

Marek Krótkiewicz

Bogumiła Hnatkowska

This book constitutes refereed proceedings of the 14th International Conference on International Conference on Computational Collective Intelligence, ICCCI 2022, held in Hammamet, Tunisia, in September 2022. The 43 full papers and 15 short papers were thoroughly reviewed and selected from 421 submissions. The papers are grouped in topical ​sections on ​collective intelligence and collective decision-making; natural language processing; deep learning; computational intelligence for multimedia understanding; computational intelligence in medical applications; applications for industry 4.0; experience enhanced intelligence to IoT and sensors; cooperative strategies for decision making and optimization; machine learning methods.

14th International Conference, ICCCI 2022, Hammamet, Tunisia, September 28–30, 2022, Proceedings

Advances in Computational Collective Intelligence

Ngoc Thanh Nguyen

Gottfried Vossen

Manuel Nunez

Adrianna Kozierkiewicz

János Botzheim

László Gulyás

This book constitutes the refereed proceedings of the 15th International Conference on Advances in Computational Collective Intelligence, ICCCI 2023, held in Budapest, Hungary, during September 27–29, 2023. The 59 full papers included in this book were carefully reviewed and selected from 218 submissions. They were organized in topical sections as follows: Collective Intelligence and Collective Decision-Making, Deep Learning Techniques, Natural Language Processing, Data Minning and Machine learning, Social Networks and Speek Communication, Cybersecurity and Internet of Things, Cooperative Strategies for Decision Making and Optimization, Digital Content Understanding and Apllication for Industry 4.0 and Computational Intelligence in Medical Applications.

15th International Conference, ICCCI 2023, Budapest, Hungary, September 27–29, 2023, Proceedings

Manuel Núñez

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.

Computational Collective Intelligence

This book addresses the challenging topic of modeling adaptive networks, which often manifest inherently complex behavior. Networks by themselves can usually be modeled using a neat, declarative, and conceptually transparent Network-Oriented Modeling approach. In contrast, adaptive networks are networks that change their structure; for example, connections in Mental Networks usually change due to learning, while connections in Social Networks change due to various social dynamics. For adaptive networks, separate procedural specifications are often added for the adaptation process. Accordingly, modelers have to deal with a less transparent, hybrid specification, part of which is often more at a programming level than at a modeling level. This book presents an overall Network-Oriented Modeling approach that makes designing adaptive network models much easier, because the adaptation process, too, is modeled in a neat, declarative, and conceptually transparent Network-Oriented Modeling manner, like the network itself. Thanks to this approach, no procedural, algorithmic, or programming skills are needed to design complex adaptive network models. A dedicated software environment is available to run these adaptive network models from their high-level specifications. Moreover, because adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive (second-order adaptation), too can be modeled just as easily. For example, this can be applied to model metaplasticity in cognitive neuroscience, or second-order adaptation in biological and social contexts. The book illustrates the usefulness of this approach via numerous examples of complex (higher-order) adaptive network models for a wide variety of biological, mental, and social processes. The book is suitable for multidisciplinary Master’s and Ph.D. students without assuming much prior knowledge, although also some elementary mathematical analysis is involved. Given the detailed information provided, it can be used as an introduction to Network-Oriented Modeling for adaptive networks. The material is ideally suited for teaching undergraduate and graduate students with multidisciplinary backgrounds or interests. Lecturers will find additional material such as slides, assignments, and software.

Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models

Laila Van Ments

This book introduces a generic approach to model the use and adaptation of mental models, including the control over this. In their mental processes, humans often make use of internal mental models as a kind of blueprints for processes that can take place in the world or in other persons. By internal mental simulation of such a mental model in their brain, they can predict and be prepared for what can happen in the future. Usually, mental models are adaptive: they can be learned, refined, revised, or forgotten, for example. Although there is a huge literature on mental models in various disciplines, a systematic account of how to model them computationally in a transparent manner is lacking. This approach allows for computational modeling of humans using mental models without a need for any algorithmic or programming skills, allowing for focus on the process of conceptualizing, modeling, and simulating complex, real-world mental processes and behaviors. The book is suitable for and is used as course material for multidisciplinary Master and Ph.D. students.

A Self-modeling Network Modeling Approach

Mental Models and Their Dynamics, Adaptation, and Control

Tibor Bosse

Moonis Ali

Koen V. Hindriks

Catholijn M. Jonker

Mark Hoogendoorn

This volume constitutes the thoroughly refereed conference proceedings of the 26th International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2013, held in Amsterdam, The Netherlands, in June 2013. The total of 71 papers selected for the proceedings were carefully reviewed and selected from 185 submissions. The papers focus on the following topics: auctions and negotiation, cognitive modeling, crowd behavior modeling, distributed systems and networks, evolutionary algorithms, knowledge representation and reasoning, pattern recognition, planning, problem solving, robotics, text mining, advances in recommender systems, business process intelligence, decision support for safety-related systems, innovations in intelligent computation and applications, intelligent image and signal processing, and machine learning methods applied to manufacturing processes and production systems.

26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Amsterdam, The Netherlands, June 17-21, 2013, Proceedings

Recent Trends in Applied Artificial Intelligence

Jan Treur

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