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Verlag: Taylor & Francis Ltd, London, 2024
ISBN 10: 0367638827 ISBN 13: 9780367638825
Sprache: Englisch
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Paperback. Zustand: new. Paperback. Distributed Artificial Intelligence (DAI) came to existence as an approach for solving complex learning, planning, and decision-making problems. When we talk about decision making, there may be some meta-heuristic methods where the problem solving may resemble like operation research. But exactly, it is not related completely to management research. The text examines representing and using organizational knowledge in DAI systems, dynamics of computational ecosystems, and communication-free interactions among rational agents. This publication takes a look at conflict-resolution strategies for nonhierarchical distributed agents, constraint-directed negotiation of resource allocations, and plans for multiple agents.Topics included plan verification, generation, and execution, negotiation operators, representation, network management problem, and conflict-resolution paradigms. The manuscript elaborates on negotiating task decomposition and allocation using partial global planning and mechanisms for assessing nonlocal impact of local decisions in distributed planning.The book will attract researchers and practitioners who are working in management and computer science, and industry persons in need of a beginner to advanced understanding of the basic and advanced concepts. This book provides a deeper understanding of the relevant aspects of AI and DAI impacting each other's efficacy for better output. It will bridge the gap between research solutions and key technologies related to data analytics to ensure Industry 4.0 requirements and at the same time ensure proper network communication and security of big data. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Verlag: Springer Nature Switzerland AG, 2022
ISBN 10: 3030855589 ISBN 13: 9783030855581
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ISBN 10: 0367638827 ISBN 13: 9780367638825
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In den WarenkorbPaperback. Zustand: new. Paperback. Distributed Artificial Intelligence (DAI) came to existence as an approach for solving complex learning, planning, and decision-making problems. When we talk about decision making, there may be some meta-heuristic methods where the problem solving may resemble like operation research. But exactly, it is not related completely to management research. The text examines representing and using organizational knowledge in DAI systems, dynamics of computational ecosystems, and communication-free interactions among rational agents. This publication takes a look at conflict-resolution strategies for nonhierarchical distributed agents, constraint-directed negotiation of resource allocations, and plans for multiple agents.Topics included plan verification, generation, and execution, negotiation operators, representation, network management problem, and conflict-resolution paradigms. The manuscript elaborates on negotiating task decomposition and allocation using partial global planning and mechanisms for assessing nonlocal impact of local decisions in distributed planning.The book will attract researchers and practitioners who are working in management and computer science, and industry persons in need of a beginner to advanced understanding of the basic and advanced concepts. This book provides a deeper understanding of the relevant aspects of AI and DAI impacting each other's efficacy for better output. It will bridge the gap between research solutions and key technologies related to data analytics to ensure Industry 4.0 requirements and at the same time ensure proper network communication and security of big data. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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ISBN 10: 3030855589 ISBN 13: 9783030855581
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In den WarenkorbHardback. Zustand: New. 2022 ed. This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users' privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federatedlearning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering.
Verlag: Springer, Berlin|Springer International Publishing|Springer, 2021
ISBN 10: 3030855589 ISBN 13: 9783030855581
Sprache: Englisch
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Zustand: New. This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy.This book presents how federated learning helps to understand and learn from user activity .
Verlag: Springer Nature Switzerland, 2023
ISBN 10: 3030855619 ISBN 13: 9783030855611
Sprache: Englisch
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Taschenbuch. Zustand: Neu. Federated Learning for IoT Applications | Satya Prakash Yadav (u. a.) | Taschenbuch | viii | Englisch | 2023 | Springer Nature Switzerland | EAN 9783030855611 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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