Complex Behavior in Evolutionary Robotics
1st Edition
Published on 30. March 2015
XX, 242 pages
978-3-11-040855-3 (ISBN)
Description
Es werden vier neue Lösungsansätze für Probleme aus dem Bereich Evolutionäre Robotik bzw. Agenten-Simulation wissenschaftlich untersucht. Von besonderem Interesse ist eine neuartige Methode zur Imitierung der natürlichen Evolution in ihrer Fähigkeit, die eigenen Mutations- und Rekombinationsoperationen während der Evolution von Robotern anzupassen.
More details
Language
English
Place of publication
Berlin/München/Boston
Germany
Target group
Professional and scholarly
US School Grade: College Graduate Student
Illustrations
86 Abbildungen, 10 s/w Tabellen
86 b/w ill., 10 b/w tbl.
File size
5,68 MB
ISBN-13
978-3-11-040855-3 (9783110408553)
DOI
http://www.degruyter.com/isbn/9783110408553
Schweitzer Classification
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Complex Behavior in Evolutionary Robotics
E-Book
03/2015
1st Edition
De Gruyter Oldenbourg
€74.95
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Lukas König
Complex Behavior in Evolutionary Robotics
Book
03/2015
1st Edition
De Gruyter
€74.95
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Content
- Intro
- 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 Mapping
- 2.1.7 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 Overview
- 3.3.2 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 Recombination
- 4.1.6 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 results
- 4.3.3 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
- 5.3 Evaluation of the Proposed Evolutionary Model
- 5.3.1 First Part - Method of Experimentation
- 5.3.2 First Part - Results and Discussion
- 5.3.3 Second Part - An Alternate Completely Evolvable Genotype-Phenotype Mapping and its Effects on Evolvability
- 5.3.4 Second Part - Method of Experimentation
- 5.3.5 Second Part - Results and Discussion
- 5.3.6 Third Part - Method of Experimentation
- 5.3.7 Third Part - Results and Discussion
- 5.4 Chapter Résumé
- 6 Data Driven Success Prediction of Evolution in Complex Environments
- 6.1 Preliminaries
- 6.2 A Model Capturing Completely Implicit Selection
- 6.2.1 Two Parents per Reproduction (CIS-2)
- 6.2.2 Eventually Stable States (k=2)
- 6.2.3 Tournament size k
- 6.2.4 Eventually Stable States (arbitrary k)
- 6.3 Extending the CIS Model to Capture Explicit Selection
- 6.4 Experiments
- 6.4.1 Evolutionary setup
- 6.4.2 Experimental Results Using the EIS Model
- 6.4.3 Remarks on Evolution in the scope of the CIS Model
- 6.5 Chapter Résumé
- 7 Conclusion
- References
- Index
