Complex behavior in evolutionary robotics /:
<!Doctype html public ""-//w3c//dtd html 4.0 transitional//en""> <html><head> <meta content=""text/html; charset=iso-8859-1"" http-equiv=content-type> <meta name=generator content=""mshtml 8.00.6001.23627""><...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston :
De Gruyter,
[2015]
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | <!Doctype html public ""-//w3c//dtd html 4.0 transitional//en""> <html><head> <meta content=""text/html; charset=iso-8859-1"" http-equiv=content-type> <meta name=generator content=""mshtml 8.00.6001.23627""></head> <body> This book examines four new approaches for problem solving in evolutionary robotics and agent simulation. Of particular interest is a new method that imitates natural evolution, allowing mutation and recombination operations to be adapted during the robotic evolution process. </body></html> |
| Beschreibung: | 1 online resource (262 pages) : illlustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9783110408553 3110408554 9783110409185 3110409186 9781523104536 1523104538 3110408546 9783110408546 |
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| 100 | 1 | |a König, Lukas, |e author. | |
| 245 | 1 | 0 | |a Complex behavior in evolutionary robotics / |c Lukas König. |
| 264 | 1 | |a Boston : |b De Gruyter, |c [2015] | |
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 online resource (262 pages) : |b illlustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
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| 546 | |a In English. | ||
| 650 | 0 | |a Evolutionary robotics. |0 http://id.loc.gov/authorities/subjects/sh98004829 | |
| 650 | 6 | |a Robotique évolutive. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Engineering (General) |2 bisacsh | |
| 650 | 7 | |a Evolutionary robotics |2 fast | |
| 655 | 4 | |a Electronic book. | |
| 758 | |i has work: |a Complex behaviour in evolutionary robotics (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFKxKWwfBfh4pgDy8RWfVP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a König, Lukas. |t Complex behavior in evolutionary robotics. |d Boston : De Gruyter, [2015] |h xx, 242 pages |z 9783110408546 |
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Datensatz im Suchindex
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| contents | 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. |
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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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a"><!Doctype html public ""-//w3c//dtd html 4.0 transitional//en""> <html><head> <meta content=""text/html; charset=iso-8859-1"" http-equiv=content-type> <meta name=generator content=""mshtml 8.00.6001.23627""></head> <body> This book examines four new approaches for problem solving in evolutionary robotics and agent simulation. 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| id | ZDB-4-EBA-ocn909907883 |
| illustrated | Illustrated |
| indexdate | 2024-10-25T16:22:37Z |
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| isbn | 9783110408553 3110408554 9783110409185 3110409186 9781523104536 1523104538 3110408546 9783110408546 |
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| physical | 1 online resource (262 pages) : illlustrations |
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| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | De Gruyter, |
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| spelling | König, Lukas, author. Complex behavior in evolutionary robotics / Lukas König. Boston : De Gruyter, [2015] ©2015 1 online resource (262 pages) : illlustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Includes bibliographical references and index. Print version record. 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. <!Doctype html public ""-//w3c//dtd html 4.0 transitional//en""> <html><head> <meta content=""text/html; charset=iso-8859-1"" http-equiv=content-type> <meta name=generator content=""mshtml 8.00.6001.23627""></head> <body> This book examines four new approaches for problem solving in evolutionary robotics and agent simulation. Of particular interest is a new method that imitates natural evolution, allowing mutation and recombination operations to be adapted during the robotic evolution process. </body></html> In English. Evolutionary robotics. http://id.loc.gov/authorities/subjects/sh98004829 Robotique évolutive. TECHNOLOGY & ENGINEERING Engineering (General) bisacsh Evolutionary robotics fast Electronic book. has work: Complex behaviour in evolutionary robotics (Text) https://id.oclc.org/worldcat/entity/E39PCFKxKWwfBfh4pgDy8RWfVP https://id.oclc.org/worldcat/ontology/hasWork Print version: König, Lukas. Complex behavior in evolutionary robotics. Boston : De Gruyter, [2015] xx, 242 pages 9783110408546 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=984276 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=984276 Volltext |
| spellingShingle | König, Lukas Complex behavior in evolutionary robotics / 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. Evolutionary robotics. http://id.loc.gov/authorities/subjects/sh98004829 Robotique évolutive. TECHNOLOGY & ENGINEERING Engineering (General) bisacsh Evolutionary robotics fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh98004829 |
| title | Complex behavior in evolutionary robotics / |
| title_auth | Complex behavior in evolutionary robotics / |
| title_exact_search | Complex behavior in evolutionary robotics / |
| title_full | Complex behavior in evolutionary robotics / Lukas König. |
| title_fullStr | Complex behavior in evolutionary robotics / Lukas König. |
| title_full_unstemmed | Complex behavior in evolutionary robotics / Lukas König. |
| title_short | Complex behavior in evolutionary robotics / |
| title_sort | complex behavior in evolutionary robotics |
| topic | Evolutionary robotics. http://id.loc.gov/authorities/subjects/sh98004829 Robotique évolutive. TECHNOLOGY & ENGINEERING Engineering (General) bisacsh Evolutionary robotics fast |
| topic_facet | Evolutionary robotics. Robotique évolutive. TECHNOLOGY & ENGINEERING Engineering (General) Evolutionary robotics Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=984276 |
| work_keys_str_mv | AT koniglukas complexbehaviorinevolutionaryrobotics |