Artificial intelligence for the internet of everything
Artificial Intelligence for the Internet of Everything considers the foundations, metrics and applications of IoE systems. It covers whether devices and IoE systems should speak only to each other, to humans or to both. Further, the book explores how IoE systems affect targeted audiences (researcher...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London :
Academic Press
[2019]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009670619906719 |
Tabla de Contenidos:
- 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 Regression
- 2.3.4. 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 Battlefield
- 3.3. 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 Considerations.
- 4.3. 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 Systems
- 5.1. 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
- Chapter 6: Trust and Human-Machine Teaming: A Qualitative Study
- 6.1. Background
- 6.1.1. Human-Machine Trust
- 6.1.2. Human-Machine Teaming
- 6.1.3. Perceived Agency
- 6.1.4. Perceived Benevolence
- 6.1.5. Perceived Task Interdependence
- 6.1.6. Relationship-Building
- 6.1.7. Communication Richness
- 6.1.8. Synchrony
- 6.2. Method
- 6.2.1. Participants
- 6.2.2. Study Description and Items
- 6.2.3. Coding Method
- 6.2.4. Trust
- 6.2.5. Human-Machine Teaming
- 6.3. Results
- 6.4. Discussion
- 6.5. Conclusion
- References
- Further Reading
- Chapter 7: The Web of Smart Entities-Aspects of a Theory of the Next Generation of the Internet of Things
- 7.1. Introduction
- 7.2. Smart Things
- 7.3. A Vision of the Next Generation of the IoT
- 7.3.1. Interlude.
- 7.4. The Use of Artificial Intelligence in the Web of Smart Entities
- 7.5. Towards a Theory of the Web of Smart Entities
- 7.5.1. Real-Time Data
- 7.5.2. Real-Time Models
- 7.5.3. Automation
- 7.5.4. Web of Smart Entities
- 7.5.5. Changing Roles of Stakeholders
- 7.6. Interacting With Automation
- 7.6.1. Fully Autonomous
- 7.6.2. Semiautonomous
- 7.6.3. Manual
- 7.6.4. Extent of Automation
- 7.7. Depth of WSE
- 7.8. Conclusions
- Acknowledgments
- References
- Chapter 8: Raising Them Right: AI and the Internet of Big Things
- 8.1. Introduction
- 8.2. ``Things Are About to Get Weird´´
- 8.3. Raise Them Right
- 8.4. Learning to Live With It
- Chapter 9: The Value of Information and the Internet of Thingsa
- 9.1. Introduction
- 9.2. The Internet of Things and Artificial Intelligence
- 9.3. Reworking Howard's Initial Example
- 9.4. Value Discussion
- 9.4.1. Generalization
- 9.5. Clairvoyance About C
- 9.6. Clairvoyance About L
- 9.7. Clairvoyance About C and L
- 9.8. Discussion
- 9.9. Conclusion
- Acknowledgments
- References
- Chapter 10: Would IOET Make Economics More Neoclassical or More Behavioral? Richard Thalers Prediction, a Revisit
- 10.1. Motivation and Introduction
- 10.2. Walrasian Auctioneer and Unmanned Markets
- 10.3. Homo Economicus vs. Homo Sapiens
- 10.3.1. Cyborgs
- 10.3.2. Trend Reversal
- 10.3.3. Trend Sustaining
- 10.4. Concluding Remarks
- Acknowledgments
- References
- Chapter 11: Accessing Validity of Argumentation of Agents of the Internet of Everything
- 11.1. Introduction
- 11.2. Representing Argumentative Discourse
- 11.3. Detecting Invalid Argumentation Patterns
- 11.4. Recognizing Communicative Discourse Trees for Argumentation
- 11.5. Assessing Validity of Extracted Argument Patterns Via Dialectical Analysis
- 11.6. Intense Arguments Dataset.
- 11.7. Evaluation of Detection and Validation of Arguments
- 11.8. Conclusions
- References
- Further Reading
- Chapter 12: Distributed Autonomous Energy Organizations: Next-Generation Blockchain Applications for Energy Infrastructure
- 12.1. Introduction to Distributed Autonomous Energy Organizations
- 12.1.1. DAEO Enablers: AI and Blockchain
- 12.2. Distributed Energy Supply Chain
- 12.3. AI-Blockchain to Secure Your Energy Supply Chain
- 12.4. Potential Blockchain Business and Implementation Challenges
- 12.5. Roadmap for When to Use Blockchain in the Energy Sector
- 12.6. The Evolution of Public Key Infrastructure Encryption
- 12.7. Why Blockchain, Why DAEO
- 12.8. Overview of the AI-Enabled Blockchain
- 12.9. Blockchain and AI Security Opportunity
- 12.10. Conclusion and Future Research
- References
- Further Reading
- Chapter 13: Compositional Models for Complex Systems
- 13.1. Introduction
- 13.2. Characteristics of Complex Systems
- 13.2.1. Heterogeneity of Components
- 13.2.2. Open Interaction
- 13.2.3. Multiplicity of Perspectives
- 13.2.4. Joint Cognition
- 13.3. System Design Is a Recursive Process
- 13.4. Inductive Datatypes and Algebraic Structures
- 13.5. Coalgebra and Infinite Datatypes
- 13.6. Operads as Compositional Architectures
- 13.7. Architectures for Learning
- 13.8. Conclusion
- Disclaimer
- References
- Chapter 14: Meta-Agents: Using Multi-Agent Networks to Manage Dynamic Changes in the Internet of Things
- 14.1. Introduction
- 14.2. Managing Complexity
- 14.3. Sentry AGEnts
- 14.4. An Illustrative Example
- 14.5. Reasoning
- 14.6. Challenges and Conclusion
- Acknowledgments
- References
- Index
- Back Cover.
