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E-Expertise: Modern Collective Intelligence (Paperback, Softcover reprint of the original 1st ed. 2014): Dmitry Gubanov,... E-Expertise: Modern Collective Intelligence (Paperback, Softcover reprint of the original 1st ed. 2014)
Dmitry Gubanov, Nikolai Korgin, Dmitry Novikov, Alexander Raikov
R3,310 Discovery Miles 33 100 Ships in 10 - 15 working days

This book focuses on organization and mechanisms of expert decision-making support using modern information and communication technologies, as well as information analysis and collective intelligence technologies (electronic expertise or simply e-expertise). Chapter 1 (E-Expertise) discusses the role of e-expertise in decision-making processes. The procedures of e-expertise are classified, their benefits and shortcomings are identified and the efficiency conditions are considered. Chapter 2 (Expert Technologies and Principles) provides a comprehensive overview of modern expert technologies. A special emphasis is placed on the specifics of e-expertise. Moreover, the authors study the feasibility and reasonability of employing well-known methods and approaches in e-expertise. Chapter 3 (E-Expertise: Organization and Technologies) describes some examples of up-to-date technologies to perform e-expertise. Chapter 4 (Trust Networks and Competence Networks) deals with the problems of expert finding and grouping by information and communication technologies. Chapter 5 (Active Expertise) treats the problem of expertise stability against any strategic manipulation by experts or coordinators pursuing individual goals. The book addresses a wide range of readers interested in management, decision-making and expert activity in political, economic, social and industrial spheres.

E-Expertise: Modern Collective Intelligence (Hardcover, 2014 ed.): Dmitry Gubanov, Nikolai Korgin, Dmitry Novikov, Alexander... E-Expertise: Modern Collective Intelligence (Hardcover, 2014 ed.)
Dmitry Gubanov, Nikolai Korgin, Dmitry Novikov, Alexander Raikov
R3,552 Discovery Miles 35 520 Ships in 10 - 15 working days

This book focuses on organization and mechanisms of expert decision-making support using modern information and communication technologies, as well as information analysis and collective intelligence technologies (electronic expertise or simply e-expertise).

Chapter 1 (E-Expertise) discusses the role of e-expertise in decision-making processes. The procedures of e-expertise are classified, their benefits and shortcomings are identified and the efficiency conditions are considered.

Chapter 2 (Expert Technologies and Principles) provides a comprehensive overview of modern expert technologies. A special emphasis is placed on the specifics of e-expertise. Moreover, the authors study the feasibility and reasonability of employing well-known methods and approaches in e-expertise.

Chapter 3 (E-Expertise: Organization and Technologies) describes some examples of up-to-date technologies to perform e-expertise.

Chapter 4 (Trust Networks and Competence Networks) deals with the problems of expert finding and grouping by information and communication technologies.

Chapter 5 (Active Expertise) treats the problem of expertise stability against any strategic manipulation by experts or coordinators pursuing individual goals.

The book addresses a wide range of readers interested in management, decision-making and expert activity in political, economic, social and industrial spheres.

Cognitive Semantics of Artificial Intelligence: A New Perspective (Paperback, 1st ed. 2021): Alexander Raikov Cognitive Semantics of Artificial Intelligence: A New Perspective (Paperback, 1st ed. 2021)
Alexander Raikov
R1,692 Discovery Miles 16 920 Ships in 12 - 17 working days

This book addresses the issue of cognitive semantics' aspects that cannot be represented by traditional digital and logical means. The problem of creating cognitive semantics can be resolved in an indirect way. The electromagnetic waves, quantum fields, beam of light, chaos control, relativistic theory, cosmic string recognition, category theory, group theory, and so on can be used for this aim. Since the term artificial intelligence (AI) appeared, various versions of logic have been created; many heuristics for neural networks deep learning have been made; new nature-like algorithms have been suggested. At the same time, the initial digital, logical, and neural network principles of representation of knowledge in AI systems have not changed a lot. The researches of these aspects of cognitive semantics of AI are based on the author's convergent methodology, which provides the necessary conditions for purposeful and sustainable convergence of decision-making.

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