Intelligent systems are required to enhance the capacities being made available to us by the internet and other computer based technologies. The theory necessary to help providing solutions to difficult problems in the construction of intelligent systems are discussed. In particular, attention is paid to situations in which the available information and data may be imprecise, uncertain, incomplete or of a linguistic nature. Various methodologies to manage such information are discussed. Among these are the probabilistic, possibilistic, fuzzy, logical, evidential and network-based frameworks.One purpose of the book is not to consider these methodologies separately, but rather to consider how they can be used cooperatively to better represent the multiplicity of modes of information. Topics in the book include representation of imperfect knowledge, fundamental issues in uncertainty, reasoning, information retrieval, learning and mining, as well as various applications.Key Features:• Tools for construction of intelligent systems • Contributions by world leading experts • Fundamental issues and applications • New technologies for web searching • Methods for modeling uncertain information • Future directions in web technologies • Transversal to methods and domains

Intelligent Decision Technologies (IDT) seeks an interchange of research on intelligent systems and intelligent technologies which enhance or improve decision making in industry, government and academia. The focus is interdisciplinary in nature, and includes research on all aspects of intelligent decision technologies, from fundamental development to the applied system. This volume represents leading research from the Third KES International Symposium on Intelligent Decision Technologies (KES IDT’11), hosted and organized by the University of Piraeus, Greece, in conjunction with KES International. The symposium was concerned with theory, design, development, implementation, testing and evaluation of intelligent decision systems. Topics include decision making theory, intelligent agents, fuzzy logic, multi-agent systems, Bayesian networks, optimization, artificial neural networks, genetic algorithms, expert systems, decision support systems, geographic information systems, case-based reasoning, time series, knowledge management systems, rough sets, spatial decision analysis, and multi-criteria decision analysis. These technologies have the potential to revolutionize decision making in many areas of management, healthcare, international business, finance, accounting, marketing, military applications, ecommerce, network management, crisis response, building design, information retrieval, and disaster recovery for a better future. The symposium was concerned with theory, design, development, implementation, testing and evaluation of intelligent decision systems. Topics include decision making theory, intelligent agents, fuzzy logic, multi-agent systems, Bayesian networks, optimization, artificial neural networks, genetic algorithms, expert systems, decision support systems, geographic information systems, case-based reasoning, time series, knowledge management systems, rough sets, spatial decision analysis, and multi-criteria decision analysis. These technologies have the potential to revolutionize decision making in many areas of management, healthcare, international business, finance, accounting, marketing, military applications, ecommerce, network management, crisis response, building design, information retrieval, and disaster recovery for a better future.

This book highlights recent research on intelligent systems and nature-inspired computing. It presents 223 selected papers from the 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022), 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 65 countries, the book offers a valuable reference guide for all researchers, students, and practitioners in the fields of computer science and engineering.

The volume "Modern Information Processing: From Theory to Applications," edited by Bernadette Bouchon-Meunier, Giulianella Coletti and Ronald Yager, is a collection of carefully selected papers drawn from the program of IPMU'04, which was held in Perugia, Italy. The book represents the cultural policy of IPMU conference which is not focused on narrow range of methodologies, but on the contrary welcomes all the theories for the management of uncertainty and aggregation of information in intelligent systems, providing a medium for the exchange of ideas between theoreticians and practitioners in these and related areas.The book is composed by 7 sections: UNCERTAINTYPREFERENCESCLASSIFICATION AND DATA MININGAGGREGATION AND MULTI-CRITERIA DECISION MAKINGKNOWLEDGE REPRESENTATION•The book contributes to enhancement of our ability to deal effectively with uncertainty in all of its manifestations. •The book can help to build brigs among theories and methods methods for the management of uncertainty. •The book addresses issues which have a position of centrality in our information-centric world. •The book presents interesting results devoted to representing knowledge: the goal is to capture the subtlety of human knowledge (richness) and to allow computer manipulation (formalization). •The book contributes to the goal: an efficient use of the information for a good decision strategy.APPLIED DOMAINS· The book contributes to enhancement of our ability to deal effectively with uncertainty in all of its manifestations.· The book can help to build brigs among theories and methods methods for the management of uncertainty.· The book addresses issues which have a position of centrality in our information-centric world.· The book presents interesting results devoted to representing knowledge: the goal is to capture the subtlety of human knowledge (richness) and to allow computer manipulation (formalization).· The book contributes to the goal: an efficient use of the information for a good decision strategy.

Intelligent systems, or artificial intelligence technologies, are playing an increasing role in areas ranging from medicine to the major manufacturing industries to financial markets. The consequences of flawed artificial intelligence systems are equally wide ranging and can be seen, for example, in the programmed trading-driven stock market crash of October 19, 1987. Intelligent Systems: Technology and Applications, Six Volume Set connects theory with proven practical applications to provide broad, multidisciplinary coverage in a single resource. In these volumes, international experts present case-study examples of successful practical techniques and solutions for diverse applications ranging from robotic systems to speech and signal processing, database management, and manufacturing.

This book presents the proceedings of the 14th International Conference on Applications of Fuzzy Systems, Soft Computing, and Artificial Intelligence Tools, ICAFS-2020, held in Budva, Montenegro, on August 27–28, 2020. It includes contributions from diverse areas of fuzzy systems, soft computing, AI tools such as uncertain computation, decision making under imperfect information, deep learning and others. The topics of the papers include theory and application of soft computing, neuro-fuzzy technology, intelligent control, deep learning–machine learning, fuzzy logic in data analytics, evolutionary computing, fuzzy logic and artificial intelligence in engineering, social sciences, business, economics, material sciences and others.

The book presents a remarkable collection of chapters covering a wide range of topics in the areas of intelligent systems and artificial intelligence, and their real-world applications. It gathers the proceedings of the Intelligent Systems Conference 2019, which attracted a total of 546 submissions from pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-review process, after which 190 were selected for inclusion in these proceedings. As intelligent systems continue to replace and sometimes outperform human intelligence in decision-making processes, they have made it possible to tackle a host of problems more effectively. This branching out of computational intelligence in several directions and use of intelligent systems in everyday applications have created the need for an international conference as a venue for reporting on the latest innovations and trends. This book collects both theory and application based chapters on virtually all aspects of artificial intelligence; presenting state-of-the-art intelligent methods and techniques for solving real-world problems, along with a vision for future research, it represents a unique and valuable asset.

Knowledge existing in modern information systems usually comes from many sources and is mapped in many ways. There is a real need for representing “knowledge pieces” as rather universal objects that should fit to multi-purpose a- ing systems. According to great number of information system’s tasks, knowledge representation is more or less detailed (e.g. some level of its granularity is - sumed). The main goal of this paper is to present chosen aspects of expressing granularity of knowledge implemented in intelligent systems. One of the main r- sons of granularity phenomena is diversification of knowledge sources, therefore the next section is devoted to this issue. 2. Heterogeneous Knowledge as a Source for Intelligent Systems Knowledge, the main element of so-called intelligent applications and systems, is very often heterogeneous. This heterogeneity concerns the origin of knowledge, its sources as well as its final forms of presentation. In this section the selected c- teria of knowledge differentiation will be presented, in the context of potential sources of knowledge acquisition. In Fig. 1 an environment of intelligent systems is shown, divided into different knowledge sources for the system. Fig. 1. Potential knowledge sources for intelligent information/reasoning system. Source: own elaboration based on (Mach, 2007) p. 24.

An Introduction to Fuzzy Logic Applications in Intelligent Systems consists of a collection of chapters written by leading experts in the field of fuzzy sets. Each chapter addresses an area where fuzzy sets have been applied to situations broadly related to intelligent systems. The volume provides an introduction to and an overview of recent applications of fuzzy sets to various areas of intelligent systems. Its purpose is to provide information and easy access for people new to the field. The book also serves as an excellent reference for researchers in the field and those working in the specifics of systems development. People in computer science, especially those in artificial intelligence, knowledge-based systems, and intelligent systems will find this to be a valuable sourcebook. Engineers, particularly control engineers, will also have a strong interest in this book. Finally, the book will be of interest to researchers working in decision support systems, operations research, decision theory, management science and applied mathematics. An Introduction to Fuzzy Logic Applications in Intelligent Systems may also be used as an introductory text and, as such, it is tutorial in nature.

In the existing literature the intersection of agent technology with soft computing is a very recent and attractive issue. The book is devoted to a unifying perspective of this topic. In contains contributions by well-known authors whose expertise is universally recognized in these crossing areas. Particular emphasis is devoted to advanced research projects involved with Web-related technologies. Fundamental topics explored in this volume are: - formal theories and logics to represent and handle imprecise communication acts among communities of agents; - soft-computing approaches to define distributed problem-solving techniques to represent and reason about large-scale control systems; - decomposition of a complex system into autonomous or semiautonomous agents through evolutionary models; - enrichment of agent programming paradigm for cooperative soft-computing processing.