Screen LibraryArtificial intelligence for the Internet of everything /
Considering the foundations, metrics and applications of Internet of Everything (IoE) systems, this book covers whether devices and IoE systems should speak only to each other, to humans or to both. --
Saved in:
| Corporate Authors: | |
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| Group Author: | ; ; ; ; |
| Published: |
Academic Press, an imprint of Elsevier,
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| Publisher Address: | London : |
| Publication Dates: |
[2019] ©2019 |
| Literature type: | eBook |
| Language: | English |
| Subjects: | |
| Online Access: |
https://www.sciencedirect.com/science/book/9780128176368 |
| Summary: |
Considering the foundations, metrics and applications of Internet of Everything (IoE) systems, this book covers whether devices and IoE systems should speak only to each other, to humans or to both. -- |
| Carrier Form: | 1 online resource |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9780128176375 0128176377 |
| Index Number: | TA347 |
| CLC: | TP18 |
| Contents: |
Front Cover; Artificial Intelligence For The Internet of Everything; Copyright; Contents; Contributors; Chapter 1: Introduction; 1.1. Introduction: IoE: IoT, IoBT, and IoIT-Background and Overview; 1.2. Introductions to the Technical Chapters; References; Chapter 2: Uncertainty Quantification in Internet of Battlefield Things; 2.1. Introduction; 2.2. Background and Motivating IoBT Scenario; 2.2.1. Detecting Vehicle-Borne IEDs in Urban Environments; 2.3. Optimization in Machine Learning; 2.3.1. Optimization Problem; 2.3.2. Stochastic Gradient Descent Algorithm 2.3.3. Example: Logistic Regression2.3.4. SGD Variants; 2.3.4.1. Mini-Batch SGD; 2.3.4.2. SGD With Momentum; 2.3.5. Nesterov's Accelerated Gradient Descent; 2.3.6. Generalized Linear Models; 2.3.7. Learning Feature Representations for Inference; 2.4. Uncertainty Quantification in Machine Learning; 2.4.1. Gaussian Process Regression; 2.4.2. Neural Network; 2.4.3. Uncertainty Quantification in Deep Neural Network; 2.5. Adversarial Learning in DNN; 2.6. Summary and Conclusion; References; Chapter 3: Intelligent Autonomous Things on the Battlefield; 3.1. Introduction 3.2. The Challenges of Autonomous Intelligence on the Battlefield3.3. AI Will Fight the Cyber Adversary; 3.4. AI Will Perceive the Complex World; 3.5. AI Enables Embodied Agents; 3.6. Coordination Requires AI; 3.7. Humans in the Ocean of Things; 3.8. Summary; References; Further Reading; Chapter 4: Active Inference in Multiagent Systems: Context-Driven Collaboration and Decentralized Purpose-Driven Team Ada ... ; 4.1. Introduction; 4.2. Energy-Based Adaptive Agent Behaviors; 4.2.1. Free Energy Principle; 4.2.2. Adaptive Behavior and Context; 4.2.3. Formal Definitions 4.2.4. Behavior Workflow and Computational Considerations4.3. Application of Energy Formalism to Multiagent Teams; 4.3.1. Motivation; 4.3.2. Problem Definition; 4.3.3. Distributed Collaborative Search Via Free Energy Minimization; 4.3.4. Adapting Team Structure; 4.4. Validation Experiments; 4.4.1. Experiment Setup; 4.4.2. Discrete Decision Making Versus Free Energy; 4.4.3. Impact of Agent Network Structure; 4.4.4. Impact of Decision Decomposition; 4.5. Conclusions; References; Further Reading Chapter 5: Policy Issues Regarding Implementations of Cyber Attack: Resilience Solutions for Cyber Physical Systems5.1. Introduction: Context; 5.2. The Need to Address Cybersecurity for Physical Systems; 5.2.1. Historic Patterns for Addressing Cybersecurity; 5.2.2. Mission-Based Cybersecurity; 5.2.3. Education of Engineers and Policy-Makers; 5.3. Cybersecurity Role and Certification of the Operators of Physical Systems; 5.4. Data Curation; 5.5. Market Incentives; 5.6. Conclusions and Recommendations; Acknowledgments; References; Further Reading |