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Produktbild: Simulation and Computational Red Teaming for Problem Solving

Simulation and Computational Red Teaming for Problem Solving

Aus der Reihe IEEE Press Series on Computational Intelligence

174,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

13.11.2019

Verlag

John Wiley & Sons

Seitenzahl

496

Maße (L/B/H)

23,1/15,5/2,8 cm

Gewicht

907 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-52717-6

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

13.11.2019

Verlag

John Wiley & Sons

Seitenzahl

496

Maße (L/B/H)

23,1/15,5/2,8 cm

Gewicht

907 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-52717-6

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Simulation and Computational Red Teaming for Problem Solving
  • Preface xi
     
    List of Figures xv
     
    List of Tables xxv
     
    Part I On Problem Solving, Computational Red Teaming, and Simulation 1
     
    1. Problem Solving, Simulation, and Computational Red Teaming 3
     
    1.1 Introduction 3
     
    1.2 Problem Solving 4
     
    1.3 Computational Red Teaming and Self-'Verification and Validation' 8
     
    2. Introduction to Fundamentals of Simulation 11
     
    2.1 Introduction 11
     
    2.2 System 14
     
    2.3 Concepts in Simulation 17
     
    2.4 Simulation Types 21
     
    2.5 Tools for Simulation 23
     
    2.6 Conclusion 24
     
    Part II Before Simulation Starts 25
     
    3. The Simulation Process 27
     
    3.1 Introduction 27
     
    3.2 Define the System and its Environment 27
     
    3.3 Build a Model 29
     
    3.4 Encode a Simulator 30
     
    3.5 Design Sampling Mechanisms 32
     
    3.6 Run Simulator Under Different Samples 33
     
    3.7 Summarise Results 33
     
    3.8 Make a Recommendation 34
     
    3.9 An Evolutionary Approach 35
     
    3.10 A Battle Simulation by Lanchester Square Law 35
     
    4. Simulation Worldview and Conflict Resolution 57
     
    4.1 Simulation Worldview 57
     
    4.2 Simultaneous Events and Conflicts in Simulation 64
     
    4.3 Priority Queue and Binary Heap 68
     
    4.4 Conclusion 72
     
    5. The Language of Abstraction and Representation 73
     
    5.1 Introduction 73
     
    5.2 Informal Representation 75
     
    5.3 Semi-formal Representation 76
     
    5.4 Formal Representation 82
     
    5.5 Finite-state Machine 86
     
    5.6 Ant in Maze Modelled by Finite-state Machine 89
     
    5.7 Conclusion 99
     
    6. Experimental Design 101
     
    6.1 Introduction 101
     
    6.2 Factor Screening 103
     
    6.3 Metamodel and Response Surface 113
     
    6.4 Input Sampling 116
     
    6.5 Output Analysis 117
     
    6.6 Conclusion 120
     
    Part III Simulation Methodologies 121
     
    7. Discrete Event Simulation 123
     
    7.1 Discrete Event Systems 123
     
    7.2 Discrete Event Simulation 126
     
    7.3 Conclusion 142
     
    8. Discrete Time Simulation 143
     
    8.1 Introduction 143
     
    8.2 Discrete Time System and Modelling 145
     
    8.3 Sample Path 148
     
    8.4 Discrete Time Simulation and Discrete Event Simulation 149
     
    8.5 A Case Study: Car-following Model 151
     
    8.6 Conclusion 154
     
    9. Continuous Simulation 157
     
    9.1 Continuous System 157
     
    9.2 Continuous Simulation 159
     
    9.3 Numerical Solution Techniques for Continuous Simulation 164
     
    9.4 System Dynamics Approach 172
     
    9.5 Combined Discrete-continuous Simulation 174
     
    9.6 Conclusion 176
     
    10. Agent-based Simulation 179
     
    10.1 Introduction 179
     
    10.2 Agent-based Simulation 181
     
    10.3 Examples of Agent-based Simulation 185
     
    10.4 Conclusion 194
     
    Part IV Simulation and Computational Red Teaming Systems 197
     
    11. Knowledge Acquisition 199
     
    11.1 Introduction 199
     
    11.2 Agent-enabled Knowledge Acquisition: Core Processes 202
     
    11.3 Human Agents 203
     
    11.4 Human-inspired Agents 208
     
    11.5 Machine Agents 211
     
    11.6 Summary Discussion and Perspectives on Knowledge Acquisition 215
     
    12. Computational Intelligence 219
     
    12.1 Introduction 219
     
    12.2 Evolutionary Computation 223
     
    12.3 Artificial Neural Networks 232
     
    12.4 Conclusion 239
     
    13. Computational Red Teaming 241
     
    13.1 Introduction 241
     
    13.2 Computational Red Teaming: The Challenge Loop 242