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• Preface; Acknowledgments; Contents; Acronyms; List of Figures; List of Tables; 1 The Art of Red Teaming ; 1.1 A Little Story; 1.2 Red Teaming; 1.2.1 Modelling; 1.2.2 Executing Exercises; 1.2.3 Deliberately Challenging; 1.2.4 Risk Lens; 1.2.5 Understanding the Space of Possibilities; 1.2.6 Exploring Non-conventional Behaviors; 1.2.7 Testing Strategies; 1.2.8 Mitigating Risk; 1.3 Success Factors of Red Teams; 1.3.1 Understanding and Analyzing the Concept of a Conflict; 1.3.2 Team Membership; 1.3.3 Time for Learning, Embodiment and Situatedness; 1.3.4 Seriousness and Commitment.

• 1.3.5 Role Continuity1.3.6 Reciprocal Interaction; 1.4 Functions of Red Teaming; 1.4.1 Discovering Vulnerabilities; 1.4.2 Discovering Opportunities; 1.4.3 Training; 1.4.4 Thinking Tools; 1.4.5 Bias Discovery; 1.4.6 Creating Future Memories and Contingency Plans; 1.4.7 Memory Washing; 1.5 Steps for Setting Up RT Exercises; 1.5.1 Setting the Purpose, Scope and Criteria of Success; 1.5.2 Designing the Exercise; 1.5.3 Conducting the Exercise; 1.5.4 Monitoring and Real-Time Analysis of the Exercise; 1.5.5 Post Analysis of the Exercise; 1.5.6 Documenting the Exercise.

• 1.5.7 Documenting Lessons Learnt on Red Teaming1.6 Ethics and Legal Dimensions of RT; 1.6.1 The RT Business Case; 1.6.2 Responsible Accountability; 1.6.2.1 Red-Teaming Stakeholder (Risk Level-Low); 1.6.2.2 Red-Teaming Communicator (Risk Level-Low); 1.6.2.3 Red-Teaming Legal Councilor (Risk Level-Low); 1.6.2.4 Red-Teaming Designer (Risk Level-Very High); 1.6.2.5 Red-Teaming Thinker (Risk Level-Very High); 1.6.2.6 Red-Teaming Technician (Risk Level-Medium); 1.6.2.7 Red-Teaming Documenter (Risk Level-Low); 1.6.2.8 Red-Teaming Auditor (Risk Level-Medium).

• 1.6.2.9 Red-Teaming Observer (Risk Level-Medium)1.6.3 The Ethics of Budget Estimation; 1.7 From Red Teaming to Computational Red Teaming; 1.7.1 Military Decision Sciences and Red Teaming; 1.7.2 Smoothing the Way Toward ComputationalRed Teaming; 1.7.3 Automating the Red-Teaming Exercise; 1.7.4 Blue-Red Simulation; 1.8 Philosophical Reflection on Assessing Intelligence; 1.8.1 The Imitation Game (Turing Test) for AssessingIntelligence; 1.8.2 Computational Red Teaming for Assessing Intelligence; References; 2 Analytics of Risk and Challenge ; 2.1 Precautions; 2.2 Risk Analytics.

• 2.2.1 Intentional Actions2.2.2 Objectives and Goals; 2.2.3 Systems; 2.2.4 Uncertainty and Risk; 2.2.5 Deliberate Actions; 2.3 Performance; 2.3.1 Behavior; 2.3.2 Skills; 2.3.3 Competency; 2.3.3.1 Need for a Standard; 2.3.3.2 Comfort vs Efficiency; 2.3.3.3 Revisiting Behavior; 2.3.4 From Gilbert's Model of Performance to a General Theory of Performance; 2.4 Challenge Analytics; 2.4.1 A Challenge is Not a Challenge; 2.4.2 Motivation and Stimulation; 2.4.3 Towards Simple Understanding of a Challenge; 2.4.4 Challenging Technologies, Concepts and Plans.

Written to bridge the information needs of management and computational scientists, this book presents the first comprehensive treatment of Computational Red Teaming (CRT).℗ The author describes an analytics environment that blends human reasoning and computational modeling to design risk-aware and evidence-based smart decision making systems. He presents the Shadow CRT Machine, which shadows the operations of an actual system to think with decision makers, challenge threats, and design remedies. This is the first book to generalize red teaming (RT) outside the military and security domains a.

• 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.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Introduction.- Introduction to Shepherding.- Introduction to Human-Swarm Teaming.- Swarm Shepherding on Ground.- Swarm Shepherding in Air.- Swarm Shepherding in Air Traffic Control.- Swarm Shepherding in Sea.- Genetic Algorithms for Optimizing Swarm Shepherding.- Reinforcement Learning for Swarm Shepherding.- Learning Classifier Systems for Swarm Shepherding.- Transparent Learning for Swarm Shepherding.- Ontology-guided Learning for Swarm Shepherding.- Mission Planning for Swarm Shepherding.- Real-Time Human Performance Analysis for Human-Swarm Teaming.- Trust for Human-Swarm Teaming.- Machine Education of Smart Shepherds.- The effect of communication range limits on shepherding performance.- Controlling the controllers: the multi shepherd swarm control problem.- Conclusion.
• (source: Nielsen Book Data)

This book draws inspiration from natural shepherding, whereby a farmer utilizes sheepdogs to herd sheep, to inspire a scalable and inherently human friendly approach to swarm control. The book discusses advanced artificial intelligence (AI) approaches needed to design smart robotic shepherding agents capable of controlling biological swarms or robotic swarms of unmanned vehicles. These smart shepherding agents are described with the techniques applicable to the control of Unmanned X Vehicles (UxVs) including air (unmanned aerial vehicles or UAVs), ground (unmanned ground vehicles or UGVs), underwater (unmanned underwater vehicles or UUVs), and on the surface of water (unmanned surface vehicles or USVs). This book proposes how smart 'shepherds' could be designed and used to guide a swarm of UxVs to achieve a goal while ameliorating typical communication bandwidth issues that arise in the control of multi agent systems. The book covers a wide range of topics ranging from the design of deep reinforcement learning models for shepherding a swarm, transparency in swarm guidance, and ontology-guided learning, to the design of smart swarm guidance methods for shepherding with UGVs and UAVs. The book extends the discussion to human-swarm teaming by looking into the real-time analysis of human data during human-swarm interaction, the concept of trust for human-swarm teaming, and the design of activity recognition systems for shepherding. Presents a comprehensive look at human-swarm teaming; Tackles artificial intelligence techniques for swarm guidance; Provides artificial intelligence techniques for real-time human performance analysis.
(source: Nielsen Book Data)

• Dual Phase Evolution.- Network Theory.- Problem Solving and Evolutionary Computation.- DPE for Network Generation.- DPE Networks and Evolutionary Dynamics.- DPE for Problem Solving.- Conclusion and Future Work.
• (source: Nielsen Book Data)

The aim of the book is to lay out the foundations and provide a detailed treatment of the subject. It will focus on two main elements in dual phase evolution: the relationship between dual phase evolution and other phase transition phenomena and the advantages of dual phase evolution in evolutionary computation and complex adaptive systems. This book will provide a coherent picture of dual phase evolution that encompasses these two elements and frameworks, methods and techniques to use this concept for problem solving.
(source: Nielsen Book Data)

• Heuristics I.- Alternative Solution Representations for the Job Shop Scheduling Problem in Ant Colony Optimisation.- Analyzing the Role of "Smart" Start Points in Coarse Search-Greedy Search.- Concealed Contributors to Result Quality - The Search Process of Ant Colony System.- Ants Guide Future Pilots.- Complex Systems I.- Information Transfer by Particles in Cellular Automata.- An Artificial Development Model for Cell Pattern Generation.- Rounds Effect in Evolutionary Games.- Modelling Architectural Visual Experience Using Non-linear Dimensionality Reduction.- An Evolutionary Benefit from Misperception in Foraging Behaviour.- Simulated Evolution of Discourse with Coupled Recurrent Networks.- How Different Hierarchical Relationships Impact Evolution.- A Dual Phase Evolution Model of Adaptive Radiation in Landscapes.- Biological Systems I.- Directed Evolution of an Artificial Cell Lineage.- An Integrated QAP-Based Approach to Visualize Patterns of Gene Expression Similarity.- Complement-Based Self-Replicated, Self-Assembled Systems (CBSRSAS).- Self-maintained Movements of Droplets with Convection Flow.- Structural Circuits and Attractors in Kauffman Networks.- The Effects of Learning on the Roles of Chance, History and Adaptation in Evolving Neural Networks.- Unsupervised Acoustic Classification of Bird Species Using Hierarchical Self-organizing Maps.- The Prisoner's Dilemma with Image Scoring on Networks: How Does a Player's Strategy Depend on Its Place in the Social Network?.- Heuristics II.- Population-Based Ant Colony Optimisation for Multi-objective Function Optimisation.- Mechanisms for Evolutionary Reincarnation.- An Evolutionary Algorithm with Spatially Distributed Surrogates for Multiobjective Optimization.- Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering.- Complex Systems II.- A Framework for the Co-evolution of Genes, Proteins and a Genetic Code Within an Artificial Chemistry Reaction Set.- In-Formation Flocking: An Approach to Data Visualization Using Multi-agent Formation Behavior.- A Principled Approach to Swarm-Based Wall-Building.- Pattern Extraction Improves Automata-Based Syntax Analysis in Songbirds.- Heuristics III.- A Modified Strategy for the Constriction Factor in Particle Swarm Optimization.- A Differential Evolution Variant of NSGA II for Real World Multiobjective Optimization.- Investigating a Hybrid Metaheuristic for Job Shop Rescheduling.- Enhancements to Extremal Optimisation for Generalised Assignment.- Biological Systems II.- Identification of Marker Genes Discriminating the Pathological Stages in Ovarian Carcinoma by Using Support Vector Machine and Systems Biology.- Ancestral DNA Sequence Reconstruction Using Recursive Genetic Algorithms.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Third Australian Conference on Artificial Life, ACAL 2007, held in Gold Coast, Australia, in December 2007. The 34 revised full papers presented were carefully reviewed and selected from 70 submissions. Research in Alife covers the main areas of biological behaviour as a metaphor for computational models, computational models that reproduce/duplicate a biological behaviour, and computational models to solve biological problems.
(source: Nielsen Book Data)

The term "artificial life" describes research into synthetic systems that possess some of the essential properties of life. This interdisciplinary field includes biologists, computer scientists, physicists, chemists, geneticists, and others. Artificial life may be viewed as an attempt to understand high-level behavior from low-level rules - for example, how the simple interactions between ants and their environment lead to complex trail-following behavior. An understanding of such relationships in particular systems can suggest novel solutions to complex real-world problems such as disease prevention, stock-market prediction, and data mining on the Internet. Since their inception in 1987, the Artificial Life meetings have grown from small workshops to truly international conferences, reflecting the field's increasing appeal to researchers in all areas of science.
(source: Nielsen Book Data)

• Heuristics I.- Alternative Solution Representations for the Job Shop Scheduling Problem in Ant Colony Optimisation.- Analyzing the Role of "Smart" Start Points in Coarse Search-Greedy Search.- Concealed Contributors to Result Quality - The Search Process of Ant Colony System.- Ants Guide Future Pilots.- Complex Systems I.- Information Transfer by Particles in Cellular Automata.- An Artificial Development Model for Cell Pattern Generation.- Rounds Effect in Evolutionary Games.- Modelling Architectural Visual Experience Using Non-linear Dimensionality Reduction.- An Evolutionary Benefit from Misperception in Foraging Behaviour.- Simulated Evolution of Discourse with Coupled Recurrent Networks.- How Different Hierarchical Relationships Impact Evolution.- A Dual Phase Evolution Model of Adaptive Radiation in Landscapes.- Biological Systems I.- Directed Evolution of an Artificial Cell Lineage.- An Integrated QAP-Based Approach to Visualize Patterns of Gene Expression Similarity.- Complement-Based Self-Replicated, Self-Assembled Systems (CBSRSAS).- Self-maintained Movements of Droplets with Convection Flow.- Structural Circuits and Attractors in Kauffman Networks.- The Effects of Learning on the Roles of Chance, History and Adaptation in Evolving Neural Networks.- Unsupervised Acoustic Classification of Bird Species Using Hierarchical Self-organizing Maps.- The Prisoner's Dilemma with Image Scoring on Networks: How Does a Player's Strategy Depend on Its Place in the Social Network?.- Heuristics II.- Population-Based Ant Colony Optimisation for Multi-objective Function Optimisation.- Mechanisms for Evolutionary Reincarnation.- An Evolutionary Algorithm with Spatially Distributed Surrogates for Multiobjective Optimization.- Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering.- Complex Systems II.- A Framework for the Co-evolution of Genes, Proteins and a Genetic Code Within an Artificial Chemistry Reaction Set.- In-Formation Flocking: An Approach to Data Visualization Using Multi-agent Formation Behavior.- A Principled Approach to Swarm-Based Wall-Building.- Pattern Extraction Improves Automata-Based Syntax Analysis in Songbirds.- Heuristics III.- A Modified Strategy for the Constriction Factor in Particle Swarm Optimization.- A Differential Evolution Variant of NSGA II for Real World Multiobjective Optimization.- Investigating a Hybrid Metaheuristic for Job Shop Rescheduling.- Enhancements to Extremal Optimisation for Generalised Assignment.- Biological Systems II.- Identification of Marker Genes Discriminating the Pathological Stages in Ovarian Carcinoma by Using Support Vector Machine and Systems Biology.- Ancestral DNA Sequence Reconstruction Using Recursive Genetic Algorithms.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Third Australian Conference on Artificial Life, ACAL 2007, held in Gold Coast, Australia, in December 2007. The 34 revised full papers presented were carefully reviewed and selected from 70 submissions. Research in Alife covers the main areas of biological behaviour as a metaphor for computational models, computational models that reproduce/duplicate a biological behaviour, and computational models to solve biological problems.
(source: Nielsen Book Data)

• 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.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Part I Physical Security and Surveillance.- Part II: Cyber Security and Intrusion Detection Systems.- Part III: Biometric Security and Authentication Systems.- Part IV: Situational Awareness and Threat Assessment.- Part V: Strategic/Mission Planning and Resource Management.
• (source: Nielsen Book Data)

This volume is an initiative undertaken by the IEEE Computational Intelligence Society's Task Force on Security, Surveillance and Defense to consolidate and disseminate the role of CI techniques in the design, development and deployment of security and defense solutions. Applications range from the detection of buried explosive hazards in a battlefield to the control of unmanned underwater vehicles, the delivery of superior video analytics for protecting critical infrastructures or the development of stronger intrusion detection systems and the design of military surveillance networks. Defense scientists, industry experts, academicians and practitioners alike will all benefit from the wide spectrum of successful applications compiled in this volume. Senior undergraduate or graduate students may also discover uncharted territory for their own research endeavors.
(source: Nielsen Book Data)

• Preface; Contents; 1 . Recreating Large-Scale Evolutionary Phenomena P.-M. Agapow; 2 . Neural Evolution for Collision Detection & Resolution in a 2D Free Flight Environment S . Alam. M . McPartland. M . Barlow. P . Lindsay. and H . A . Abbass; 3 . Cooperative Coevolution of Genotype-Phenotype Mappings to Solve Epistatic Optimization Problems L . T . Bui. H . A . Abbass, and D . Essam; 4 . Approaching Perfect Mixing in a Simple Model of the Spread of an Infectious Disease D . Chu and J . Rowe.

Artificial life is now a recognized discipline of research with many important applications and software tools. However, many theoretical issues remain unresolved. This book brings together a cross-section of key developments in artificial life, which in turn gives us new insight into the theory of complex systems.The central ideas of the book surround genetics and evolution in an artificial life framework. Topics covered include maintenance of genetic diversity, hierarchical structures and stability of ecosystems. Underpinning these topics are key theoretical developments surrounding network complexity, the development of pattern languages for complex networks and a deeper understanding of the edge of chaos where complex systems live. Practical applications include optimization, gene regulatory networks, modeling the spread of disease and the evolution of ageing.The reader will gain an insight into the mathematical techniques at the core of artificial life and encounter a sufficient diversity of applications to stimulate new directions in their own field.
(source: Nielsen Book Data)

• 1. Recreating large-scale evolutionary phenomena / P.-M. Agapow
• 2. Neural evolution for collision detection & resolution in a 2D free flight environment / S. Alam ... [et al.]
• 3. Cooperative coevolution of genotype-phenotype mappings to solve epistatic optimization problems / L. T. Bui. H. A. Abbass, and D. Essam
• 4. Approaching perfect mixing in a simple model of the spread of an infectious disease / D . Chu and J . Rowe
• 5. The formation of hierarchical structures in a pseudo-spatial co-evolutionary artificial life environment / D. Cornforth. D. G. Green and J. Awburn
• 6. Perturbation analysis : a complex systems pattern / N. Geard. K. Willadsen and J. Wiles
• 7. A simple genetic algorithm for studies of Mendelian populations / C . Gondro and J. C. M. Magalhaes
• 8. Roles of rule-priority evolution in animat models / K. A. Hawick, H. A. James and C. J. Scogings
• 9. Gauging ALife : emerging complex systems / K. Kitto
• 10. Localisation of critical transition phenomena in cellular automata rule-space / A. Lafusa and T. Bossomaier
• 11. Issues in the scalability of gate-level morphogenetic evolvable hardware / J. Lee and J. Sitte
• 12. Phenotype diversity objectives for graph grammar evolution / M. H. Luerssen
• 13. An ALife investigation on the origins of dimorphic parental investments / S. Mascaro. K. B. Korb and A. E. Nicholson
• 14. Local structure and stability of model and real world ecosystems / D. Newth and D. Cornforth
• 15. Quantification of emergent behaviors induced by feedback resonance of chaos / A. Patti, M. Lungarella and Y. Kuniyoshi
• 16. A dynamic optimisation approach for ant colony optimisation using the multidimensional Knapsack problem / M. Randall
• 17. Maintaining explicit diversity within individual ant colonies / M. Randall
• 18. Evolving gene regulatory networks for cellular morphogenesis / T. Rudge and N. Geard
• 19. Complexity of networks / R. K. Standish
• 20. A generalised technique for building 2D structures with robot swarms / R. L. Stewart and R. A. Russell
• 21. H-ABC : a scalable dynamic routing algorithm / B. Tatomir and L. J. M. Rothkrantz
• 22. Describing DNA automata using an artificial chemistry based on pattern matching and recombination / T . Watanabe ... [et al.]
• 23. Towards a network pattern language for complex systems / J. Watson ... [et al.]
• 24. The evolution of aging / O. G. Woodberry. K. B. Korb and A. E. Nicholson
• 25. Evolving capability requirements in WISDOM-II / A. Yang ... [et al.].

Artificial life is now a recognized discipline of research with many important applications and software tools. However, many theoretical issues remain unresolved. This book brings together a cross-section of key developments in artificial life, which in turn gives us new insight into the theory of complex systems.The central ideas of the book surround genetics and evolution in an artificial life framework. Topics covered include maintenance of genetic diversity, hierarchical structures and stability of ecosystems. Underpinning these topics are key theoretical developments surrounding network complexity, the development of pattern languages for complex networks and a deeper understanding of the edge of chaos where complex systems live. Practical applications include optimization, gene regulatory networks, modeling the spread of disease and the evolution of ageing.The reader will gain an insight into the mathematical techniques at the core of artificial life and encounter a sufficient diversity of applications to stimulate new directions in their own field.
(source: Nielsen Book Data)