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Complex behavior in evolutionary robotics /
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| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boston :
De Gruyter,
[2015]
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Acknowledgements; Contents; List of Figures; List of Tables; List of Notations; 1 Introduction; 1.1 Evolutionary Robotics and Evolutionary Swarm Robotics; 1.2 Further Classifications; 1.3 Challenges of ER; 1.4 Structure and Major Contributions of the Thesis; 2 Robotics, Evolution and Simulation; 2.1 Evolutionary Training of Robot Controllers; 2.1.1 Two Views on Selection in ER and ESR; 2.1.2 Classification of Fitness Functions in ER; 2.1.3 The Bootstrap Problem; 2.1.4 The Reality Gap; 2.1.5 Decentralized Online Evolution in ESR.
- 2.1.6 Evolvability, Controller Representation and the Genotype-Phenotype Mapping2.1.7 Controller Representation; 2.1.8 Recombination Operators; 2.1.9 Success Prediction in ESR; 2.2 Agent-based Simulation; 3 The Easy Agent Simulation; 3.1 History of the Easy Agent Simulation Framework; 3.2 Basic Idea and Architectural Concept; 3.2.1 Overview; 3.2.2 Preliminaries; 3.2.3 Classification of the Architecture; 3.2.4 The SPI Architecture from an MVC Perspective; 3.2.5 Comparison of the SPI Architecture with State-of-the-Art ABS Frameworks; 3.3 Implementation of the SPI within the EAS Framework.
- 3.3.1 Overview3.3.2 Plugins; 3.3.3 Master Schedulers; 3.3.4 The classes SimulationTime and Wink; 3.3.5 The Interface EASRunnable; 3.3.6 "Everything is an Agent": a Philosophical Decision; 3.3.7 Running a Simulation; 3.3.8 Getting Started; 3.4 A Comparative Study and Evaluation of the EAS Framework; 3.4.1 Method of Experimentation; 3.4.2 Results and Discussion; 3.5 Chapter Résumé; 4 Evolution Using Finite State Machines; 4.1 Theoretical Foundations; 4.1.1 Preliminaries; 4.1.2 Definition of the MARB Controller Model; 4.1.3 Encoding MARBs; 4.1.4 Mutation and Hardening.
- 4.1.5 Selection and Recombination4.1.6 Fitness calculation; 4.1.7 The Memory Genome: a Decentralized Elitist Strategy; 4.1.8 Fitness Adjustment after Mutation, Recombination and Reactivation of the Memory Genome; 4.1.9 The Robot Platforms; 4.2 Preliminary Parameter Adjustment using the Example of Collision Avoidance; 4.2.1 Specification of Evolutionary Parameters; 4.2.2 Method of Experimentation; 4.2.3 Evaluation and Discussion; 4.2.4 Concluding Remarks; 4.3 A Comprehensive Study Using the Examples of Collision Avoidance and Gate Passing; 4.3.1 Method of Experimentation.
- 4.3.2 Experimental results4.3.3 Concluding remarks; 4.4 Experiments With Real Robots; 4.4.1 Evolutionary Model; 4.4.2 Method of Experimentation; 4.4.3 Results and Discussion; 4.4.4 Concluding Remarks; 4.5 Chapter Résumé; 5 Evolution and the Genotype-Phenotype Mapping; 5.1 Overview of the Presented Approach; 5.2 A Completely Evolvable Genotype-Phenotype Mapping; 5.2.1 Definition of (complete) evolvability; 5.2.2 Properties of ceGPM-based genotypic encodings; 5.2.3 The Translator Model MAPT and the Course of Evolution; 5.2.4 Genotypic and Phenotypic Spaces; 5.2.5 Evolutionary Operators.