Holdings: Model-driven development methodology and domain-specific languages for the design of artificial intelligence in cyber-physical systems

Model-driven development methodology and domain-specific languages for the design of artificial intelligence in cyber-physical systems:

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Bibliographic Details
Main Author:	Kusmenko, Evgeny (Author)
Format:	Thesis Book
Language:	English
Published:	Düren Shaker 2021
Series:	Aachener Informatik-Berichte, Software-Engineering Band 49
Subjects:	Künstliche Intelligenz Modellgetriebene Entwicklung Domänenspezifische Programmiersprache Cyber-physisches System DSLs AI MDSE Machine Learning Hochschulschrift
Online Access:	Inhaltsverzeichnis
Physical Description:	xiii, 324 Seiten Illustrationen 24 cm x 17 cm, 510 g
ISBN:	9783844082869 3844082867

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Record in the Search Index

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adam_text	CONTENTS 1 INTRODUCTION 1 1.1 MOTIVATION ........................................................................................................ 1 1.2 RESEARCH QUESTIONS ............................................................................................ 2 1.3 CPS BASICS ........................................................................................................ 2 1.3.1 AUTONOMOUS DRIVING ARCHITECTURES .................................................... 2 1.3.2 CONTROL .................................................................................................. 4 1.3.3 MACHINE LEARNING IN AUTONOMOUS DRIVING ........................................ 4 1.4 REQUIREMENTS ..................................................................................................... 5 1.5 THESIS STRUCTURE ............................................................................................... 6 1.6 PUBLICATIONS ........................................................................................................ 7 1.7 PRELIMINARIES ..................................................................................................... 10 1.7.1 NOTATION .................................................................................................. 10 1.7.2 MODEL-DRIVEN ENGINEERING AND DOMAIN SPECIFIC LANGUAGES .... 12 1.7.3 THE MONTICORE LANGUAGE WORKBENCH .............................................. 13 1.7.4 SI UNITS .................................................................................................. 15 1.7.5 MODEL-DRIVEN ENGINEERING PROCESSES IN THE AUTOMOTIVE DOMAIN . 17 1.7.6 COMPONENT & CONNECTOR MODELING .................................................... 19 2 EMBEDDEDMONTIARC 23 2.1 REQUIREMENTS ..................................................................................................... 23 2.2 THE DATA TYPE SYSTEM .................................................................................. 24 2.2.1 PRIMITIVE DATA TYPES ......................................................................... 24 2.2.2 VECTORS, MATRICES AND CUBES ............................................................. 26 2.2.3 MATRIX PROPERTIES ............................................................................... 26 2.2.4 SI UNITS IN EMBEDDEDMONTIARC .......................................................... 29 2.2.5 STRUCTS AND ENUMS ............................................................................... 29 2.3 STATIC ARCHITECTURE DESCRIPTION ...................................................................... 31 2.3.1 COMPONENTS, PORTS AND CONNECTORS ................................................. 31 2.3.2 ARRAYS AND CONNECTION PATTERNS ....................................................... 33 2.3.3 EXECUTION SEMANTICS ............................................................................. 35 2.4 MONTIMATH ........................................................................................................ 40 2.4.1 BASIC SYNTAX ......................................................................................... 40 2.4.2 DERIVING MATRIX PROPERTIES FOR CONCRETE MATRICES .............................. 42 2.4.3 DERIVING MATRIX PROPERTIES FOR OPERATIONS ......................................... 45 XIII 2.4.4 EMBEDDEDMONTIARCMATH .................................................................... 46 2.4.5 OPTIMIZATION IN MONTIMATH ................................................................. 47 2.4.6 COMPONENT VARIABILTY FOR SELF-ADAPTABLE SYSTEMS ............................. 49 2.5 CODE GENERATOR ................................................................................................... 53 2.6 MODEL-DRIVEN UNIT-TESTING .............................................................................. 56 2.6.1 THE STREAM LANGUAGE .......................................................................... 56 2.6.2 THE MAVEN STREAMTEST PLUGIN .............................................................. 58 2.6.3 SIMULATION ............................................................................................. 59 3 DYNAMICS ASPECTS OF EMBEDDEDMONTIARC 63 3.1 COOPERATIVE AGENTS ............................................................................................. 63 3.2 BACKGROUND & REQUIREMENTS .......................................................................... 65 3.3 DYNAMIC ADLS ................................................................................................... 68 3.3.1 BRIEF OVERVIEW ....................................................................................... 68 3.3.2 REQUIREMENTS ASSESSMENT .................................................................... 72 3.4 EMBEDDEDMONTIARC DYNAMICS ....................................................................... 73 3.4.1 EMAD EXECUTION SEMANTICS .............................................................. 74 3.4.2 DATA-TRIGGERED INTERNAL RECONFIGURATION ............................................ 74 3.4.3 SERVICE-BASED EXTERNAL RECONFIGURATION ............................................ 79 3.4.4 MODELING COMPONENT PIPELINES ........................................................... 86 3.4.5 RECONFIGURATION VIEWS AND GRAPHICAL NOTATION ................................. 89 3.4.6 REMARKS ON ARCHITECTURAL CONSISTENCY ............................................... 90 3.5 CONCLUSION ......................................................................................................... 93 4 MODELING ARTIFICIAL NEURAL NETWORKS WITH MONTIANNA 95 4.1 DEEP LEARNING FOR AUTONOMOUS SYSTEMS .......................................................... 95 4.1.1 SUPERVISED MACHINE LEARNING FOUNDATIONS ........................................ 95 4.1.2 NEURAL NETWORKS .................................................................................... 96 4.1.3 TRAINING OF LAYERED NEURAL NETWORKS .................................................. 99 4.1.4 DEEP NETWORK ARCHITECTURES .................................................................... 101 4.2 REQUIREMENTS OF A DEEP LEARNING MODELING FRAMEWORK FOR CYBER-PHYSICAL SYSTEMS .................................................................................................................. 106 4.3 OVERVIEW OF DEEP LEARNING FRAMEWORKS .......................................................... 108 4.4 MACHINE LEARNING MODELING FRAMEWORKS .......................................................... 112 4.5 THE MONTIANNA FRAMEWORK ................................................................................ 116 4.6 AN OVERVIEW OF MODELING LANGUAGES ................................................................ 117 4.6.1 THE COMPILER TOOLCHAIN .......................................................................... 119 4.6.2 THE GENERATED ARTIFACTS .......................................................................... 120 4.7 MODELING FEEDFORWARD NEURAL ARCHITECTURES WITH CNNARC ........................... 121 4.7.1 DEFINING A STAND-ALONE NETWORK .......................................................... 121 4.7.2 MODELING LAYERS AND NETWORKS ............................................................. 122 4.7.3 CODE REUSE IN CNNARC .......................................................................... 128 4.8 MODELING RECURRENT NEURAL NETWORKS ................................................................ 134 4.8.1 BASIC CONCEPTS ........................................................................................ 134 4.8.2 MODELING AN ENCODER-DECODER NETWORK ............................................. 135 4.9 MODELING TRAINING .............................................................................................. 140 4.9.1 THE COMPOSED MODEL ............................................................................ 143 4.10 EMBEDDEDMONTIARCDL ..................................................................................... 144 4.10.1 CNNARC AS IMPLEMENTATION LANGUAGE FOR EMBEDDEDMONTIARC COM PONENTS ........................................................................................ 145 4.10.2 MODELING THE DATASET ............................................................................ 146 4.10.3 THE MNISTCALCULATOR EXAMPLE ......................................................... 147 4.10.4 MODELING A DIRECT PERCEPTION AUTONOMOUS VEHICLE CONTROLLER . . . 151 5 MODELING DEEP REINFORCEMENT LEARNING ARCHITECTURES 157 5.1 FOUNDATIONS OF DEEP REINFORCEMENT LEARNING ................................................ 157 5.2 REQUIREMENTS ....................................................................................................... 159 5.3 RELATED WORK ....................................................................................................... 160 5.4 MODELING REINFORCEMENT LEARNING ................................................................... 164 5.5 MODELING THE FUNCTION APPROXIMATORS ............................................................ 164 5.6 MODELING THE TRAINING ........................................................................................ 168 5.6.1 GENERAL REINFORCEMENT LEARNING PARAMETERS .................................... 168 5.6.2 THE TORCS TRAINING MODEL ................................................................ 171 5.6.3 REWARD FUNCTION ..................................................................................... 173 5.7 ENVIRONMENT ....................................................................................................... 175 5.8 CODE GENERATION ................................................................................................. 177 5.9 MODELING GENERATIVE ADVERSARIAL NETWORKS ................................................... 179 5.10 EVALUATION ............................................................................................................. 180 5.10.1 TORCS AND OPEN AL GYM ................................................................... 180 5.10.2 DECISION MAKING IN FORESTRY 5.0 ............................................................ 183 5.11 CONCLUSION AND FUTURE WORK ............................................................................... 188 6 MODELING DISTRIBUTED ARCHITECTURES 191 6.1 THE NEED FOR DISTRIBUTED SYSTEMS ................................................................... 191 6.2 EXISTING APPROACHES FOR MIDDLEWARE INTEGRATION .......................................... 192 6.3 RUNNING EXAMPLE AND USE CASES ........................................................................ 194 6.4 REQUIREMENTS ....................................................................................................... 196 6.5 TAGGING-BASED MIDDLEWARE MODELING ................................................................ 197 6.6 CODE GENERATOR COMPOSITION ............................................................................ 202 6.7 EVALUATION ............................................................................................................. 210 6.8 AUTOMATING MODEL SLICING FOR DISTRIBUTED DEPLOYMENT ................................. 211 6.8.1 MOTIVATION .............................................................................................. 211 6.8.2 COMPONENT CLUSTERING ............................................................................ 212 6.8.3 WEIGHTS ..................................................................................................... 215 6.8.4 ENCORPORATING STRUCTURAL CONSTRAINTS .................................................. 215 6.8.5 RELATED WORK ON AUTOMATED DEPLOYMENT ............................................ 216 6.9 CONCLUSION AND FUTURE WORK ............................................................................ 218 7 CONCLUSION 219 BIBLIOGRAPHY 223 A DIAGRAMS AND LISTINGS 251 B FURTHER DOCUMENTATION 261 B.L CNNARC LAYER CLASSES ......................................................................................... 261 B.2 CNNTRAIN EVALUATION METRICS ............................................................................ 265 B.3 CNNTRAIN FOR REINFORCEMENT LEARNING ............................................................. 266 B.3.1 GENERAL REINFORCEMENT LEARNING PARAMETERS .................................... 266 B.3. 2 DQN EXCLUSIVE PARAMETERS ................................................................... 268 B.3. 3 TD3 EXCLUSIVE PARAMETERS ................................................................... 269 B.3. 4 TRAINING RESULTS ...................................................................................... 270 B.4 MONTICORE 5 GRAMMARS ...................................................................................... 271 LIST OF FIGURES 291 LIST OF TABLES 297
adam_txt	CONTENTS 1 INTRODUCTION 1 1.1 MOTIVATION . 1 1.2 RESEARCH QUESTIONS . 2 1.3 CPS BASICS . 2 1.3.1 AUTONOMOUS DRIVING ARCHITECTURES . 2 1.3.2 CONTROL . 4 1.3.3 MACHINE LEARNING IN AUTONOMOUS DRIVING . 4 1.4 REQUIREMENTS . 5 1.5 THESIS STRUCTURE . 6 1.6 PUBLICATIONS . 7 1.7 PRELIMINARIES . 10 1.7.1 NOTATION . 10 1.7.2 MODEL-DRIVEN ENGINEERING AND DOMAIN SPECIFIC LANGUAGES . 12 1.7.3 THE MONTICORE LANGUAGE WORKBENCH . 13 1.7.4 SI UNITS . 15 1.7.5 MODEL-DRIVEN ENGINEERING PROCESSES IN THE AUTOMOTIVE DOMAIN . 17 1.7.6 COMPONENT & CONNECTOR MODELING . 19 2 EMBEDDEDMONTIARC 23 2.1 REQUIREMENTS . 23 2.2 THE DATA TYPE SYSTEM . 24 2.2.1 PRIMITIVE DATA TYPES . 24 2.2.2 VECTORS, MATRICES AND CUBES . 26 2.2.3 MATRIX PROPERTIES . 26 2.2.4 SI UNITS IN EMBEDDEDMONTIARC . 29 2.2.5 STRUCTS AND ENUMS . 29 2.3 STATIC ARCHITECTURE DESCRIPTION . 31 2.3.1 COMPONENTS, PORTS AND CONNECTORS . 31 2.3.2 ARRAYS AND CONNECTION PATTERNS . 33 2.3.3 EXECUTION SEMANTICS . 35 2.4 MONTIMATH . 40 2.4.1 BASIC SYNTAX . 40 2.4.2 DERIVING MATRIX PROPERTIES FOR CONCRETE MATRICES . 42 2.4.3 DERIVING MATRIX PROPERTIES FOR OPERATIONS . 45 XIII 2.4.4 EMBEDDEDMONTIARCMATH . 46 2.4.5 OPTIMIZATION IN MONTIMATH . 47 2.4.6 COMPONENT VARIABILTY FOR SELF-ADAPTABLE SYSTEMS . 49 2.5 CODE GENERATOR . 53 2.6 MODEL-DRIVEN UNIT-TESTING . 56 2.6.1 THE STREAM LANGUAGE . 56 2.6.2 THE MAVEN STREAMTEST PLUGIN . 58 2.6.3 SIMULATION . 59 3 DYNAMICS ASPECTS OF EMBEDDEDMONTIARC 63 3.1 COOPERATIVE AGENTS . 63 3.2 BACKGROUND & REQUIREMENTS . 65 3.3 DYNAMIC ADLS . 68 3.3.1 BRIEF OVERVIEW . 68 3.3.2 REQUIREMENTS ASSESSMENT . 72 3.4 EMBEDDEDMONTIARC DYNAMICS . 73 3.4.1 EMAD EXECUTION SEMANTICS . 74 3.4.2 DATA-TRIGGERED INTERNAL RECONFIGURATION . 74 3.4.3 SERVICE-BASED EXTERNAL RECONFIGURATION . 79 3.4.4 MODELING COMPONENT PIPELINES . 86 3.4.5 RECONFIGURATION VIEWS AND GRAPHICAL NOTATION . 89 3.4.6 REMARKS ON ARCHITECTURAL CONSISTENCY . 90 3.5 CONCLUSION . 93 4 MODELING ARTIFICIAL NEURAL NETWORKS WITH MONTIANNA 95 4.1 DEEP LEARNING FOR AUTONOMOUS SYSTEMS . 95 4.1.1 SUPERVISED MACHINE LEARNING FOUNDATIONS . 95 4.1.2 NEURAL NETWORKS . 96 4.1.3 TRAINING OF LAYERED NEURAL NETWORKS . 99 4.1.4 DEEP NETWORK ARCHITECTURES . 101 4.2 REQUIREMENTS OF A DEEP LEARNING MODELING FRAMEWORK FOR CYBER-PHYSICAL SYSTEMS . 106 4.3 OVERVIEW OF DEEP LEARNING FRAMEWORKS . 108 4.4 MACHINE LEARNING MODELING FRAMEWORKS . 112 4.5 THE MONTIANNA FRAMEWORK . 116 4.6 AN OVERVIEW OF MODELING LANGUAGES . 117 4.6.1 THE COMPILER TOOLCHAIN . 119 4.6.2 THE GENERATED ARTIFACTS . 120 4.7 MODELING FEEDFORWARD NEURAL ARCHITECTURES WITH CNNARC . 121 4.7.1 DEFINING A STAND-ALONE NETWORK . 121 4.7.2 MODELING LAYERS AND NETWORKS . 122 4.7.3 CODE REUSE IN CNNARC . 128 4.8 MODELING RECURRENT NEURAL NETWORKS . 134 4.8.1 BASIC CONCEPTS . 134 4.8.2 MODELING AN ENCODER-DECODER NETWORK . 135 4.9 MODELING TRAINING . 140 4.9.1 THE COMPOSED MODEL . 143 4.10 EMBEDDEDMONTIARCDL . 144 4.10.1 CNNARC AS IMPLEMENTATION LANGUAGE FOR EMBEDDEDMONTIARC COM PONENTS . 145 4.10.2 MODELING THE DATASET . 146 4.10.3 THE MNISTCALCULATOR EXAMPLE . 147 4.10.4 MODELING A DIRECT PERCEPTION AUTONOMOUS VEHICLE CONTROLLER . . . 151 5 MODELING DEEP REINFORCEMENT LEARNING ARCHITECTURES 157 5.1 FOUNDATIONS OF DEEP REINFORCEMENT LEARNING . 157 5.2 REQUIREMENTS . 159 5.3 RELATED WORK . 160 5.4 MODELING REINFORCEMENT LEARNING . 164 5.5 MODELING THE FUNCTION APPROXIMATORS . 164 5.6 MODELING THE TRAINING . 168 5.6.1 GENERAL REINFORCEMENT LEARNING PARAMETERS . 168 5.6.2 THE TORCS TRAINING MODEL . 171 5.6.3 REWARD FUNCTION . 173 5.7 ENVIRONMENT . 175 5.8 CODE GENERATION . 177 5.9 MODELING GENERATIVE ADVERSARIAL NETWORKS . 179 5.10 EVALUATION . 180 5.10.1 TORCS AND OPEN AL GYM . 180 5.10.2 DECISION MAKING IN FORESTRY 5.0 . 183 5.11 CONCLUSION AND FUTURE WORK . 188 6 MODELING DISTRIBUTED ARCHITECTURES 191 6.1 THE NEED FOR DISTRIBUTED SYSTEMS . 191 6.2 EXISTING APPROACHES FOR MIDDLEWARE INTEGRATION . 192 6.3 RUNNING EXAMPLE AND USE CASES . 194 6.4 REQUIREMENTS . 196 6.5 TAGGING-BASED MIDDLEWARE MODELING . 197 6.6 CODE GENERATOR COMPOSITION . 202 6.7 EVALUATION . 210 6.8 AUTOMATING MODEL SLICING FOR DISTRIBUTED DEPLOYMENT . 211 6.8.1 MOTIVATION . 211 6.8.2 COMPONENT CLUSTERING . 212 6.8.3 WEIGHTS . 215 6.8.4 ENCORPORATING STRUCTURAL CONSTRAINTS . 215 6.8.5 RELATED WORK ON AUTOMATED DEPLOYMENT . 216 6.9 CONCLUSION AND FUTURE WORK . 218 7 CONCLUSION 219 BIBLIOGRAPHY 223 A DIAGRAMS AND LISTINGS 251 B FURTHER DOCUMENTATION 261 B.L CNNARC LAYER CLASSES . 261 B.2 CNNTRAIN EVALUATION METRICS . 265 B.3 CNNTRAIN FOR REINFORCEMENT LEARNING . 266 B.3.1 GENERAL REINFORCEMENT LEARNING PARAMETERS . 266 B.3. 2 DQN EXCLUSIVE PARAMETERS . 268 B.3. 3 TD3 EXCLUSIVE PARAMETERS . 269 B.3. 4 TRAINING RESULTS . 270 B.4 MONTICORE 5 GRAMMARS . 271 LIST OF FIGURES 291 LIST OF TABLES 297
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author	Kusmenko, Evgeny
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genre	(DE-588)4113937-9 Hochschulschrift gnd-content
genre_facet	Hochschulschrift
id	DE-604.BV047664139
illustrated	Illustrated
index_date	2024-07-03T18:53:24Z
indexdate	2024-07-10T09:18:40Z
institution	BVB
institution_GND	(DE-588)1064118135
isbn	9783844082869 3844082867
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-033048904
oclc_num	1302325108
open_access_boolean
owner	DE-83 DE-355 DE-BY-UBR
owner_facet	DE-83 DE-355 DE-BY-UBR
physical	xiii, 324 Seiten Illustrationen 24 cm x 17 cm, 510 g
publishDate	2021
publishDateSearch	2021
publishDateSort	2021
publisher	Shaker
record_format	marc
series	Aachener Informatik-Berichte, Software-Engineering
series2	Aachener Informatik-Berichte, Software-Engineering
spelling	Kusmenko, Evgeny Verfasser (DE-588)1247121062 aut Model-driven development methodology and domain-specific languages for the design of artificial intelligence in cyber-physical systems vorgelegt von Evgeny Kusmenko Düren Shaker 2021 xiii, 324 Seiten Illustrationen 24 cm x 17 cm, 510 g txt rdacontent n rdamedia nc rdacarrier Aachener Informatik-Berichte, Software-Engineering Band 49 Dissertation RWTH Aachen University 2021 Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Modellgetriebene Entwicklung (DE-588)4832365-2 gnd rswk-swf Domänenspezifische Programmiersprache (DE-588)7585264-0 gnd rswk-swf Cyber-physisches System (DE-588)1069505412 gnd rswk-swf DSLs AI MDSE Machine Learning (DE-588)4113937-9 Hochschulschrift gnd-content Modellgetriebene Entwicklung (DE-588)4832365-2 s Domänenspezifische Programmiersprache (DE-588)7585264-0 s Künstliche Intelligenz (DE-588)4033447-8 s Cyber-physisches System (DE-588)1069505412 s DE-604 Shaker Verlag (DE-588)1064118135 pbl Aachener Informatik-Berichte, Software-Engineering Band 49 (DE-604)BV040516036 49 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033048904&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20211021 DE-101 https://d-nb.info/provenance/plan#vlb
spellingShingle	Kusmenko, Evgeny Model-driven development methodology and domain-specific languages for the design of artificial intelligence in cyber-physical systems Aachener Informatik-Berichte, Software-Engineering Künstliche Intelligenz (DE-588)4033447-8 gnd Modellgetriebene Entwicklung (DE-588)4832365-2 gnd Domänenspezifische Programmiersprache (DE-588)7585264-0 gnd Cyber-physisches System (DE-588)1069505412 gnd
subject_GND	(DE-588)4033447-8 (DE-588)4832365-2 (DE-588)7585264-0 (DE-588)1069505412 (DE-588)4113937-9
title	Model-driven development methodology and domain-specific languages for the design of artificial intelligence in cyber-physical systems
title_auth	Model-driven development methodology and domain-specific languages for the design of artificial intelligence in cyber-physical systems
title_exact_search	Model-driven development methodology and domain-specific languages for the design of artificial intelligence in cyber-physical systems
title_exact_search_txtP	Model-driven development methodology and domain-specific languages for the design of artificial intelligence in cyber-physical systems
title_full	Model-driven development methodology and domain-specific languages for the design of artificial intelligence in cyber-physical systems vorgelegt von Evgeny Kusmenko
title_fullStr	Model-driven development methodology and domain-specific languages for the design of artificial intelligence in cyber-physical systems vorgelegt von Evgeny Kusmenko
title_full_unstemmed	Model-driven development methodology and domain-specific languages for the design of artificial intelligence in cyber-physical systems vorgelegt von Evgeny Kusmenko
title_short	Model-driven development methodology and domain-specific languages for the design of artificial intelligence in cyber-physical systems
title_sort	model driven development methodology and domain specific languages for the design of artificial intelligence in cyber physical systems
topic	Künstliche Intelligenz (DE-588)4033447-8 gnd Modellgetriebene Entwicklung (DE-588)4832365-2 gnd Domänenspezifische Programmiersprache (DE-588)7585264-0 gnd Cyber-physisches System (DE-588)1069505412 gnd
topic_facet	Künstliche Intelligenz Modellgetriebene Entwicklung Domänenspezifische Programmiersprache Cyber-physisches System Hochschulschrift
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033048904&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV040516036
work_keys_str_mv	AT kusmenkoevgeny modeldrivendevelopmentmethodologyanddomainspecificlanguagesforthedesignofartificialintelligenceincyberphysicalsystems AT shakerverlag modeldrivendevelopmentmethodologyanddomainspecificlanguagesforthedesignofartificialintelligenceincyberphysicalsystems

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