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E-raamat: Simulation and Computational Red Teaming for Problem Solving

(University of New South Wales Canberra, Australia), (University of New South Wales Canberra, Australia), (University of New South Wales Canberra, Australia)
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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 books 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.
Preface xi
List of Figures
xv
List of Tables
xxv
PART I ON PROBLEM SOLVING, COMPUTATIONAL RED TEAMING, AND SIMULATION
1(24)
1 Problem Solving, Simulation, and Computational Red Teaming
3(8)
1.1 Introduction
3(1)
1.2 Problem Solving
4(4)
1.3 Computational Red Teaming and Self-'Verification and Validation'
8(3)
2 Introduction to Fundamentals of Simulation
11(14)
2.1 Introduction
11(3)
2.2 System
14(3)
2.3 Concepts in Simulation
17(4)
2.4 Simulation Types
21(2)
2.5 Tools for Simulation
23(1)
2.6 Conclusion
24(1)
PART II BEFORE SIMULATION STARTS
25(96)
3 The Simulation Process
27(30)
3.1 Introduction
27(1)
3.2 Define the System and its Environment
27(2)
3.3 Build a Model
29(1)
3.4 Encode a Simulator
30(2)
3.5 Design Sampling Mechanisms
32(1)
3.6 Run Simulator Under Different Samples
33(1)
3.7 Summarise Results
33(1)
3.8 Make a Recommendation
34(1)
3.9 An Evolutionary Approach
35(1)
3.10 A Battle Simulation by Lanchester Square Law
35(22)
4 Simulation World view and Conflict Resolution
57(16)
4.1 Simulation Worldview
57(7)
4.2 Simultaneous Events and Conflicts in Simulation
64(4)
4.3 Priority Queue and Binary Heap
68(4)
4.4 Conclusion
72(1)
5 The Language of Abstraction and Representation
73(28)
5.1 Introduction
73(2)
5.2 Informal Representation
75(1)
5.3 Semi-formal Representation
76(6)
5.4 Formal Representation
82(4)
5.5 Finite-state Machine
86(3)
5.6 Ant in Maze Modelled by Finite-state Machine
89(10)
5.7 Conclusion
99(2)
6 Experimental Design
101(20)
6.1 Introduction
101(2)
6.2 Factor Screening
103(10)
6.3 Metamodel and Response Surface
113(3)
6.4 Input Sampling
116(1)
6.5 Output Analysis
117(3)
6.6 Conclusion
120(1)
PART III SIMULATION METHODOLOGIES
121(76)
7 Discrete Event Simulation
123(20)
7.1 Discrete Event Systems
123(3)
7.2 Discrete Event Simulation
126(16)
7.3 Conclusion
142(1)
8 Discrete Time Simulation
143(14)
8.1 Introduction
143(2)
8.2 Discrete Time System and Modelling
145(3)
8.3 Sample Path
148(1)
8.4 Discrete Time Simulation and Discrete Event Simulation
149(2)
8.5 A Case Study: Car-following Model
151(3)
8.6 Conclusion
154(3)
9 Continuous Simulation
157(22)
9.1 Continuous System
157(2)
9.2 Continuous Simulation
159(5)
9.3 Numerical Solution Techniques for Continuous Simulation
164(8)
9.4 System Dynamics Approach
172(2)
9.5 Combined Discrete-continuous Simulation
174(2)
9.6 Conclusion
176(3)
10 Agent-based Simulation
179(18)
10.1 Introduction
179(2)
10.2 Agent-based Simulation
181(4)
10.3 Examples of Agent-based Simulation
185(9)
10.4 Conclusion
194(3)
PART IV SIMULATION AND COMPUTATIONAL RED TEAMING SYSTEMS
197(56)
11 Knowledge Acquisition
199(20)
11.1 Introduction
199(3)
11.2 Agent-enabled Knowledge Acquisition: Core Processes
202(1)
11.3 Human Agents
203(5)
11.4 Human-inspired Agents
208(3)
11.5 Machine Agents
211(4)
11.6 Summary Discussion and Perspectives on Knowledge Acquisition
215(4)
12 Computational Intelligence
219(22)
12.1 Introduction
219(4)
12.2 Evolutionary Computation
223(9)
12.3 Artificial Neural Networks
232(7)
12.4 Conclusion
239(2)
13 Computational Red Teaming
241(12)
13.1 Introduction
241(1)
13.2 Computational Red Teaming: The Challenge Loop
242(1)
13.3 Computational Red Teaming Objects
243(1)
13.4 Computational Red Teaming Purposes
244(1)
13.5 Objectives of Red Teaming Exercises in Computational Red Teaming Purposes
245(1)
13.6 Discovering Biases
246(1)
13.7 Computational Red Teaming Lifecycle: A Systematic Approach to Red Teaming Exercises
247(4)
13.8 Conclusion
251(2)
PART V SIMULATION AND COMPUTATIONAL RED TEAMING APPLICATIONS
253(96)
14 Computational Red Teaming for Battlefield Management
255(8)
14.1 Introduction
255(1)
14.2 Battlefield Management Simulation
256(5)
14.3 Conclusion
261(2)
15 Computational Red Teaming for Air Traffic Management
263(10)
15.1 Introduction
263(1)
15.2 Air Traffic Simulation
263(7)
15.3 A Human-in-the-loop Application
270(1)
15.4 Conclusion
271(2)
16 Computational Red Teaming Application for Skill-based Performance Assessment
273(28)
16.1 Introduction
273(1)
16.2 Cognitive Task Analysis-based Skill Modelling and Assessment Methodology
274(2)
16.3 Sudoku and Human Players
276(4)
16.4 Sudoku and Computational Solvers
280(3)
16.5 The Proposed Skill-based Computational Solver
283(10)
16.6 Discussion of Simulation Results
293(7)
16.7 Conclusions
300(1)
17 Computational Red Teaming for Driver Assessment
301(32)
17.1 Introduction
301(2)
17.2 Background on Cognitive Agents
303(3)
17.3 The Society of Mind Agent
306(6)
17.4 Society of Mind Agents in an Artificial Environment
312(13)
17.5 Case Study
325(5)
17.6 Conclusion
330(3)
18 Computational Red Teaming for Trusted Autonomous Systems
333(16)
18.1 Introduction
333(1)
18.2 Trust for Influence and Shaping
334(1)
18.3 The Model
335(7)
18.4 Experiment Design and Parameter Settings
342(2)
18.5 Results and Discussion
344(3)
18.6 Conclusion
347(2)
A Probability and Statistics in Simulation
349(48)
A.1 Foundation of Probability and Statistics
349(20)
A.2 Useful Distributions
369(21)
A.3 Mathematical Characteristics of Random Variables
390(6)
A.4 Conclusion
396(1)
B Sampling and Random Numbers
397(38)
B.1 Introduction
397(3)
B.2 Random Number Generator
400(8)
B.3 Testing Random Number Generators
408(5)
B.4 Approaches to Generating Random Variates
413(3)
B.5 Generating Random Variates
416(7)
B.6 Monte Carlo Method
423(9)
B.7 Conclusion
432(3)
Bibliography 435(24)
Index 459
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.
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