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.

Table of Contents:
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