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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
