XCS for Self-awareness in Autonomous Computing Systems / by Tim Hansmeier ; [Supervisors: Prof. Dr. Marco Platzner, Reviewers: Prof. Dr. Marco Platzner, Prof. Dr. David Andrews, Prof. Dr. Sybille Hellebrand, Oral examination committee: Prof. Dr. Marco Platzner, Prof. Dr. David Andrews, Prof. Dr. Sybille Hellebrand, Jun.-Prof. Dr. Sebastian Peitz, Dr. Heinrich Riebler]. Paderborn, 2023
Inhalt
- Acknowledgments
- Abstract
- Zusammenfassung
- Publications
- Contents
- List of Figures
- List of Tables
- Acronyms
- 1 Introduction
- 2 Computational Self-Awareness
- 2.1 Key Concepts
- 2.2 Reference Architecture
- 2.3 Related Concepts
- 2.4 Applications of Self-* Computing
- 3 The Learning Classifier System XCS
- 3.1 Algorithmic Description of XCS
- 3.2 The Working Mechanism of XCS
- 3.3 XCS Extensions
- 3.4 XCS for Computational Self-Awareness
- 4 Experimental Comparison of Autonomous Explore/Exploit Strategies
- 4.1 Explore/Exploit Strategies
- 4.2 Experimental Setup
- 4.3 Single-Environment Evaluation
- 4.4 Multi-Environment Evaluation
- 4.5 Dynamic Environment Evaluation
- 4.6 Summary and Deployment Guidelines
- 4.7 Conclusion and Future Work
- 5 Safety Guarantees through Forbidden Classifiers
- 5.1 Related Work
- 5.2 Forbidden Classifiers
- 5.3 Experimental Setup
- 5.4 Experimental Evaluation: 6-Multiplexer
- 5.5 Experimental Evaluation: Maze
- 5.6 Experimental Evaluation: Classification
- 5.7 Conclusion and Future Work
- 6 Case Study: XCS for Frequency Control
- 6.1 Related Work
- 6.2 Application Scenario
- 6.3 Experimental Setup
- 6.4 Experimental Results
- 6.5 Environmental Characteristics and Behavior of XCS
- 6.6 Conclusion and Future Work
- 7 Conclusion and Future Work
- Bibliography
- Colophon