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Solving complex problems in real-world contexts, such as financial investment planning or mining large data collections, involves many different sub-tasks, each of which requires different techniques. To deal with such problems, a great diversity of intelligent techniques are available, including traditional techniques like expert systems approaches and soft computing techniques like fuzzy logic, neural networks, or genetic algorithms. These techniques are complementary approaches to intelligent information processing rather than competing ones, and thus better results in problem solving are achieved when these techniques are combined in hybrid intelligent systems. Multi-Agent Systems are ideally suited to model the manifold interactions among the many different components of hybrid intelligent systems. This book introduces agent-based hybrid intelligent systems and presents a framework and methodology allowing for the development of such systems for real-world applications. The authors focus on applications in financial investment planning and data mining.
Engineering Intelligent Hybrid Multi-Agent Systems is about building intelligent hybrid systems. Included is coverage of applications and design concepts related to fusion systems, transformation systems and combination systems. These applications are in areas involving hybrid configurations of knowledge-based systems, case-based reasoning, fuzzy systems, artificial neural networks, genetic algorithms, and in knowledge discovery and data mining. Through examples and applications a synergy of these subjects is demonstrated. The authors introduce a multi-agent architectural theory for engineering intelligent associative hybrid systems. The architectural theory is described at both the task structure level and the computational level. This problem-solving architecture is relevant for developing knowledge agents and information agents. An enterprise-wide system modeling framework is outlined to facilitate forward and backward integration of systems developed in the knowledge, information, and data engineering layers of an organization. In the modeling process, software engineering aspects like agent oriented analysis, design and reuse are developed and described. Engineering Intelligent Hybrid Multi-Agent Systems is the first book in the field to provide details of a multi-agent architecture for building intelligent hybrid systems.
Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. This book comprising of 17 chapters offers a step-by-step introduction (in a chronological order) to the various modern computational intelligence tools used in practical problem solving. Staring with different search techniques including informed and uninformed search, heuristic search, minmax, alpha-beta pruning methods, evolutionary algorithms and swarm intelligent techniques; the authors illustrate the design of knowledge-based systems and advanced expert systems, which incorporate uncertainty and fuzziness. Machine learning algorithms including decision trees and artificial neural networks are presented and finally the fundamentals of hybrid intelligent systems are also depicted. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques, machine learning and data mining would find the comprehensive coverage of this book invaluable.
The two LNAI volumes 6678 and 6679 constitute the proceedings of the 6th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2011, held in Wroclaw, Poland, in May 2011. The 114 papers published in these proceedings were carefully reviewed and selected from 241 submissions. They are organized in topical sessions on hybrid intelligence systems on logistics and intelligent optimization; metaheuristics for combinatorial optimization and modelling complex systems; hybrid systems for context-based information fusion; methods of classifier fusion; intelligent systems for data mining and applications; systems, man, and cybernetics; hybrid artificial intelligence systems in management of production systems; hybrid artificial intelligent systems for medical applications; and hybrid intelligent approaches in cooperative multi-robot systems.
This book highlights recent advances in the design of hybrid intelligent systems based on nature-inspired optimization and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction, and optimization of complex problems. The book is divided into seven main parts, the first of which addresses theoretical aspects of and new concepts and algorithms based on type-2 and intuitionistic fuzzy logic systems. The second part focuses on neural network theory, and explores the applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The book’s third part presents enhancements to meta-heuristics based on fuzzy logic techniques and describes new nature-inspired optimization algorithms that employ fuzzy dynamic adaptation of parameters, while the fourth part presents diverse applications of nature-inspired optimization algorithms. In turn, the fifth part investigates applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. The sixth part examines new optimization algorithms and their applications. Lastly, the seventh part is dedicated to the design and application of different hybrid intelligent systems.
Hybrid Intelligent Techniques for Pattern Analysis and Understanding outlines the latest research on the development and application of synergistic approaches to pattern analysis in real-world scenarios. An invaluable resource for lecturers, researchers, and graduates students in computer science and engineering, this book covers a diverse range of hybrid intelligent techniques, including image segmentation, character recognition, human behavioral analysis, hyperspectral data processing, and medical image analysis.
This volume offers a general view of recent conceptual developments of Soft Computing (SC). It presents successful new applications of SC to real-world problems leading to better performance than "traditional" methods. The edited volume covers a wide spectrum of applications including areas such as: robotic dynamic systems, non-linear plants, manufacturing systems, and time series prediction.
This book highlights recent research on intelligent systems and nature-inspired computing. It presents 132 selected papers from the 21st International Conference on Intelligent Systems Design and Applications (ISDA 2021), which was held online. The ISDA is a premier conference in the field of computational intelligence, and the latest installment brought together researchers, engineers and practitioners whose work involves intelligent systems and their applications in industry. Including contributions by authors from 34 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.
Symbolic processing has limitations highlighted by the symbol grounding problem. Computational processing methods, like fuzzy logic, neural networks, and statistical methods have appeared to overcome these problems. However, they also suffer from drawbacks in that, for example, multi-stage inference is difficult to implement. Deep fusion of symbolic and computational processing is expected to open a new paradigm for intelligent systems. Symbolic processing and computational processing should interact at all abstract or computational levels. For this undertaking, attempts to combine, hybridize, and fuse these processing methods should be thoroughly investigated and the direction of novel fusion approaches should be clarified. This book contains the current status of this attempt and also discusses future directions.