Simulation and Computational Red Teaming for Problem Solving

An authoritative guide to computer simulation grounded in a multi-disciplinary approach for solving complex problems

Simulation and Computational Red Teaming for Problem Solving offers a review of computer simulation that is grounded in a multi-disciplinary approach. The authors present the theoretical foundations of simulation and modeling paradigms from the perspective of an analyst. The book provides the fundamental background information needed for designing and developing consistent and useful simulations. In addition to this basic information, the authors explore several advanced topics.

The book’s advanced topics demonstrate how modern artificial intelligence and computational intelligence concepts and techniques can be combined with various simulation paradigms for solving complex and critical problems. Authors examine the concept of Computational Red Teaming to reveal how the combined fundamentals and advanced techniques are used successfully for solving and testing complex real-world problems. This important book:

•    Demonstrates how computer simulation and Computational Red Teaming support each other for solving complex problems

•    Describes the main approaches to modeling real-world phenomena and embedding these models into computer simulations

•    Explores how a number of advanced artificial intelligence and computational intelligence concepts are used in conjunction with the fundamental aspects of simulation

Written for researchers and students in the computational modelling and data analysis fields, Simulation and Computational Red Teaming for Problem Solving covers the foundation and the standard elements of the process of building a simulation and explores the simulation topic with a modern research approach.

JIANGJUN TANG, PHD, is a Lecturer at the School of Engineering and Information Technology at the University of New South Wales Canberra, Australia.

GEORGE LEU, PHD, is a Senior Research Associate at the School of Engineering and Information Technology at the University of New South Wales Canberra, Australia.

HUSSEIN A. ABBASS, PHD, is a Professor at the School of Engineering and Information Technology at the University of New South Wales Canberra, Australia.

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

13.3 Computational Red Teaming Objects 243

13.4 Computational Red Teaming Purposes 244

13.5 Objectives of Red Teaming Exercises in Computational Red Teaming Purposes 245

13.6 Discovering Biases 246

13.7 Computational Red Teaming Lifecycle: A Systematic Approach to Red Teaming Exercises 247

13.8 Conclusion 251

Part V Simulation and Computational Red Teaming Applications 253

14. Computational Red Teaming for Battlefield Management 255

14.1 Introduction 255

14.2 Battlefield Management Simulation 256

14.3 Conclusion 261

15. Computational Red Teaming for Air Traffic Management 263

15.1 Introduction 263

15.2 Air Traffic Simulation 263

15.3 A Human-in-the-loop Application 270

15.4 Conclusion 271

16. Computational Red Teaming Application for Skill-based Performance Assessment 273

16.1 Introduction 273

16.2 Cognitive Task Analysis-based Skill Modelling and Assessment Methodology 274

16.3 Sudoku and Human Players 276

16.4 Sudoku and Computational Solvers 280

16.5 The Proposed Skill-based Computational Solver 283

16.6 Discussion of Simulation Results 293

16.7 Conclusions 300

17. Computational Red Teaming for Driver Assessment 301

17.1 Introduction 301

17.2 Background on Cognitive Agents 303

17.3 The Society of Mind Agent 306

17.4 Society of Mind Agents in an Artificial Environment 312

17.5 Case Study 325

17.6 Conclusion 330

18. Computational Red Teaming for Trusted Autonomous Systems 333

18.1 Introduction 333

18.2 Trust for Influence and Shaping 334

18.3 The Model 335

18.4 Experiment Design and Parameter Settings 342

18.5 Results and Discussion 344

18.6 Conclusion 347

A. Probability and Statistics in Simulation 349

A.1 Foundation of Probability and Statistics 349

A.2 Useful Distributions 369

A.3 Mathematical Characteristics of Random Variables 390

A.4 Conclusion 396

B Sampling and Random Numbers 397

B.1 Introduction 397

B.2 Random Number Generator 400

B.3 Testing Random Number Generators 408

B.4 Approaches to Generating Random Variates 413

B.5 Generating Random Variates 416

B.6 Monte Carlo Method 423

B.7 Conclusion 432

Bibliography 435

Index 459

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