Kooperativer Bibliotheksverbund

UID:

Format: 1 online resource (xiii, 322 pages) : , illustrations (some color).

Edition: First edition.

ISBN: 9781003038467 , 1003038468 , 9781000262056 , 1000262057 , 9781000262087 , 1000262081 , 9781000262117 , 1000262111

Series Statement: Internet of everything (ioe): security and privacy paradigm

Content: "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"--

Additional Edition: Print version: Distributed artificial intelligence Boca Raton : CRC Press, 2020. ISBN 9780367466657

Language: English

Subjects: Computer Science

Keywords: Electronic books. ; Electronic books.

URL: https://www.taylorfrancis.com/books/9781003038467

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (337 pages)

Edition: 1st ed.

ISBN: 9781000262117

Series Statement: Internet of Everything (IoE) Series

Content: 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

Note: Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- Chapter 1 Distributed Artificial Intelligence -- 1.1 Introduction -- 1.2 Why Distributed Artificial Intelligence? -- 1.3 Characteristics of Distributed Artificial Intelligence -- 1.4 Planning of DAI Multi-Agents -- 1.5 Coordination among Multi-Agents -- 1.5.1 Forestalling Mobocracy or Confusion -- 1.5.2 Meeting Overall Requirements -- 1.5.3 Distributed Skill, Resources, and Data -- 1.5.4 Dependency among the Agents -- 1.5.5 Efficiency -- 1.6 Communication Modes among the Agents -- 1.7 Categories of RPC -- 1.8 Participation of Multi-Agents -- 1.8.1 Fully Cooperative Architecture -- 1.8.2 Partial Cooperative Architecture -- 1.9 Applications of DAI -- 1.9.1 Electricity Distribution -- 1.9.2 Telecommunications Systems -- 1.9.3 Database Technologies for Service Order Processing -- 1.9.3.1 Concurrent Engineering -- 1.9.3.2 Weather Monitoring -- 1.9.3.3 Intelligent Traffic Control -- 1.10 Conclusion -- References -- Chapter 2 Intelligent Agents -- 2.1 Introduction -- 2.2 Need for Evolving Agents in Evolutionary Software Systems -- 2.2.1 Change of Requirements -- 2.2.2 Need for an Evolving System -- 2.2.3 Software System -- 2.2.4 Evolving Software System -- 2.3 Agents -- 2.3.1 Evolving Agents -- 2.3.2 Agent Architecture -- 2.3.3 Application Domain -- 2.3.3.1 Types of Agents -- References -- Chapter 3 Knowledge-Based Problem-Solving: How AI and Big Data Are Transforming Health Care -- 3.1 Introduction -- 3.2 The Role of AI, Big Data, and IoT in Health Care -- 3.3 Image-Based Diagnosis -- 3.4 Big Data Analytics Process Using Machine Learning -- 3.5 Discussion -- 3.6 Conclusion -- References -- Chapter 4 Distributed Artificial Intelligence for Document Retrieval -- 4.1 Introduction -- 4.2 Proposed Research , 14.2 Background and Motivation , 4.2.1 Improving Precision -- 4.3 General-Purpose Ranking -- 4.4 Structure-Weighted Ranking -- 4.5 The Structure-Weighted/Learned Function -- 4.6 Improving Recall and Precision -- 4.6.1 Stemming -- 4.6.2 Relevance Feedback -- 4.6.3 Thesaurus -- 4.7 Preliminary Results -- 4.8 Scope for Distributed AI in This Process -- 4.9 Benefits of Decentralized Search Engines -- 4.10 Discussion -- 4.11 Conclusion -- References -- Chapter 5 Distributed Consensus -- 5.1 Introduction -- 5.2 Nakamoto Consensus -- 5.2.1 Nakamoto Consensus Working -- 5.2.1.1 Proof of Work -- 5.2.1.2 Block Selection -- 5.2.1.3 Scarcity -- 5.2.1.4 Incentive Structure -- 5.2.2 Security of Bitcoin -- 5.2.3 The PoW Algorithm -- 5.2.4 Proof of Stake -- 5.2.5 Proof of Burn -- 5.2.6 Difficulty Level -- 5.2.7 Sybil Attack -- 5.2.7.1 Eclipse Attack -- 5.2.8 Hyperledger Fabric: A Blockchain Development -- 5.3 Conclusions and Discussions -- References -- Chapter 6 DAI for Information Retrieval -- 6.1 Introduction -- 6.2 Distributed Problem-Solving -- 6.3 Multiagents -- 6.4 A Multiagent Approach for Peer-to-Peer-Based Information Recoupment Systems -- 6.4.1 A Mediator-Free Framework -- 6.4.2 Agent-View Algorithm -- 6.4.3 Distributed Search Algorithms -- 6.5 Blackboard Model -- 6.6 DIALECT 2: An Information Recoupment System -- 6.6.1 The Control in Blackboard Systems -- 6.6.2 Control in DIALECT 2 -- 6.6.2.1 The Linguistic Parser -- 6.6.2.2 The Reformation Module -- 6.7 Analysis and Discussion -- 6.8 Conclusion -- References -- Chapter 7 Decision Procedures -- 7.1 Motivation -- 7.2 Introduction -- 7.3 Distributed Artificial Intelligence -- 7.4 Applying Artificial Intelligence to Decision-Making -- 7.5 Automated Decision-Making by AI -- 7.5.1 Impact of Automated Decision System -- 7.5.2 Forms of Automated Decision System -- 7.5.3 Application of Automated Decision System -- 7.5.4 Cyber Privacy Concerns , 7.5.5 Discussion and Future Impact -- 7.6 Cooperation in Multi-Agent Environments -- 7.6.1 Notations and Workflow -- 7.6.2 Action Independence -- 7.7 Game Theory Scenario -- 7.8 Data-Driven or AI-Driven -- 7.8.1 Human Judgment -- 7.8.2 Data-Driven Decision-Making -- 7.8.3 Working of Data-Driven Decisions -- 7.8.4 AI-Driven Decision-Making -- 7.8.5 Leveraging Human and AI-Driven Workflows Together -- 7.9 Calculative Rationality -- 7.10 Meta-Level Rationality and Meta-Reasoning -- 7.11 The Role of Decision Procedures in Distributed Decision-Making -- 7.12 Advantages of Distributed Decision-Making -- 7.13 Optimization Decision Theory -- 7.13.1 Multi-Level (Hierarchical) Algorithms -- 7.14 Dynamic Programming -- 7.15 Network Flow -- 7.16 Large-Scale Decision-Making (LSDM) -- 7.16.1 Key Elements in an LSDM Model -- 7.17 Conclusion -- Reference -- Chapter 8 Cooperation through Communication in a Distributed Problem-Solving Network -- 8.1 Introduction -- 8.2 Distributed Control System -- 8.2.1 Design Decisions -- 8.2.2 Host Node Software Communication -- 8.2.3 Convolutional Software Node Network -- 8.2.4 Assessment of Distributed Situation -- 8.2.5 Computer-Aided Control Engineering (CACE) -- 8.2.6 Knowledge Base -- 8.2.7 Training Dataset -- 8.3 Motivation and Development of the ICE Architecture -- 8.3.1 History of ICE Model -- 8.3.1.1 Operators on Information States -- 8.3.1.2 Relations to Observable Quantum Mechanics -- 8.3.1.3 The Influence of Sociology and Intentional States -- 8.3.2 Requirements of a Theory of Animal and Robotics Communication -- 8.4 A Brief Conceptual History of Formal Semantics -- 8.4.1 Tarski Semantics -- 8.4.2 Possible World Semantics -- 8.4.3 Semantics of Temporal Logic -- 8.4.4 Limitations of Kripke Possible World Semantics -- 8.5 Related Work -- 8.6 Dynamic Possible World Semantics -- 8.7 Situation Semantics and Pragmatics , 8.8 Modeling Distributed AI Systems as a Distributed Goal Search Problem -- 8.9 Discussion -- 8.10 Conclusion -- References -- Chapter 9 Instantiating Descriptions of Organizational Structures -- 9.1 Introduction -- 9.1.1 Example of Organizational Structure -- 9.1.2 Purpose -- 9.1.3 Components -- 9.1.3.1 Obligations -- 9.1.3.2 Assets -- 9.1.3.3 Information -- 9.1.3.4 Apparatuses -- 9.1.3.5 Experts and Subcontractors -- 9.1.4 Relation between Components -- 9.1.4.1 Correspondence -- 9.1.4.2 Authority -- 9.1.4.3 Area, Proximity, and so on -- 9.1.5 Description of the Organizational Structures with EFIGE -- 9.1.6 The Constraint Solution Algorithm -- 9.1.6.1 Requirement Propagation -- 9.1.6.2 Imperative Utility -- 9.2 Comparative Study of Organization Structure -- 9.3 Conclusion -- References -- Chapter 10 Agora Architecture -- 10.1 Introduction -- 10.1.1 Characteristics of System for which Agora Is Useful -- 10.2 Architecture of Agora -- 10.3 Agora's Virtual Machine -- 10.3.1 Element Cliques (EC) -- 10.3.2 Knowledge Source (KS) -- 10.3.3 Mapping of KS into Mach layer -- 10.3.4 Frameworks -- 10.3.4.1 Typical Framework Tools -- 10.3.4.2 Knowledge Base: CFrame -- 10.4 Examples of Systems Built Using Agora -- 10.4.1 Intelligent Transport System (ITS) -- 10.4.1.1 Architecture of Agora ITS Framework -- 10.4.1.2 Agora ITS Applications -- 10.4.2 CMU Speech Recognition System -- 10.5 Application of Agora as a Minimal Distributed Protocol for E-Commerce -- 10.5.1 Basic Protocol -- 10.5.2 Accounts -- 10.5.3 Transactions -- 10.5.4 Properties of Agora Protocol -- 10.5.4.1 Minimal -- 10.5.4.2 Distribution -- 10.5.4.3 Authentication -- 10.5.4.4 Security -- 10.5.5 Enhanced Protocol to Regulate Fraud -- 10.5.5.1 New Message -- 10.5.5.2 Batch Processing -- 10.5.5.3 Selection of Parameter -- 10.5.5.4 Online Arbitration -- References , Chapter 11 Test Beds for Distributed AI Research -- 11.1 Introduction -- 11.2 Background -- 11.3 Tools and Methodology -- 11.3.1 MACE -- 11.3.1.1 MACE System -- 11.3.2 Actor Model -- 11.3.3 MICE Testbed -- 11.3.4 ARCHON -- 11.3.4.1 Multiagent Environment -- 11.3.4.2 The ARCHON Architecture -- 11.3.5 Distributed Vehicle Monitoring Testbed (DVMST) -- 11.3.6 AGenDA Testbed -- 11.3.6.1 Architectural Level -- 11.3.6.2 System Development Level -- 11.3.6.3 Other Testbeds for DAI -- 11.4 Conclusion -- References -- Chapter 12 Real-Time Framework Competitive Distributed Dilemma -- 12.1 Introduction -- 12.2 Real-Time Route Guidance Distributed System Framework -- 12.3 Experts Cooperating -- 12.4 A Distributed Problem-Solving Perspective -- 12.5 Caveats for Cooperation -- 12.6 Task Sharing -- 12.7 Result-Sharing -- 12.8 Task-Sharing and Result-Sharing: A Comparative Analysis -- 12.9 Conclusion -- References -- Chapter 13 Comparative Studied Based on Attack Resilient and Efficient Protocol with Intrusion Detection System Based on Deep Neural Network for Vehicular System Security -- 13.1 Introduction -- 13.2 Related Work -- 13.3 Background -- 13.3.1 Processing Phase -- 13.3.2 Training Phase -- 13.4 Intrusion Detection System -- 13.5 IDS with Machine Learning -- 13.6 Proposed Technique -- 13.6.1 Proposed Deep Neural Network Intrusion Detection System -- 13.6.2 Training the Deep Neural Network Structure -- 13.6.2.1 ANN Parameters -- 13.6.2.2 Input Layer's Neurons -- 13.6.2.3 Hidden Layer's Neurons -- 13.6.2.4 Output Layer's Neurons -- 13.6.2.5 Transfer Function -- 13.7 Simulation Parameters -- 13.7.1 Average End-to-End Delay -- 13.7.2 Average Energy Consumption -- 13.7.3 Average Network Throughput -- 13.7.4 Packet Delivery Ratio (PDR) -- 13.8 Conclusion -- References -- Chapter 14 A Secure Electronic Voting System Using Decentralized Computing -- 14.1 Introduction

Additional Edition: Print version Yadav, Satya Prakash Distributed Artificial Intelligence Milton : Taylor & Francis Group,c2020 ISBN 9780367466657

Language: English

Keywords: Electronic books ; Electronic books

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URL: FULL  ((OIS Credentials Required))