John R. Clymer, PhD, is a Professor at California State University at Fullerton.

"Simulation-Based Engineering of Complex Systems provides a methodology and a tool to gain understanding on complex systems through simulation based engineering. The software tool (ExtendSimTM) which is provided as a CD works with the OpEMCSS library and gives the readers an opportunity of experimentation." (Journal of Artificial Societies and Social Simulation, July 2010)

• Preface xiiiAcknowledgments xviiOverview xix1 Introduction to Simulation-Based Systems Engineering 11.1 Definition of Complex Systems 31.1.1 Exercise: Model a Goal-Oriented Activity 61.1.2 Agent-Based System Architectures 91.1.3 Simulation and AI-Based System Design 111.1.4 Expansionism Versus Reductionism 121.1.5 Summary 151.2 Using Simulation to Understand Complex Systems 151.2.1 ExtendSim Discrete-Event Simulation User Environment and OpEMCSS Overview 151.2.2 Simulation Model Development Procedure 171.2.3 Simulation Programs: How Serial and Parallel Process Models Work 211.2.4 Sensitivity Analysis 291.3 Bringing Complex Systems into Being 301.3.1 Definition of Systems Engineering 311.3.2 Levels of System Description 331.3.3 Systems Engineering Life Cycle 351.3.4 Simulation of the System Development Process 381.3.5 Simulation-Based Systems Engineering 461.4 Summary 47Problems 50References 53Bibliography 532 Simulation Concepts and Building Blocks 552.1 Statistical Aspects of Simulation 562.1.1 Convergence Theorems 572.1.2 Uniform Random-Number Generator 582.1.3 Discrete Probability Distributions 592.1.4 Goodness-of-Fit Test 602.1.5 Generation of Random Variables 622.2 OpEM Graphical Modeling Language 642.2.1 Petri Nets 652.2.2 OpEM Graphs 682.3 OpEM Parallel Process Simulations 722.3.1 Sequential Process Event 762.3.2 Split Event 782.3.3 Complex Assemble Event 802.3.4 Simple Assemble Event 832.3.5 Comparison of Petri Nets and OpEM Graphs 842.4 OpEMCSS Simulation of Context-Sensitive Systems 862.4.1 Types of CSS Process Interactions and Timeline Analysis 862.4.2 How ExtendSim Has Been Modified to Implement the OpEM Language 882.4.3 How OpEMCSS Blocks Work Together to Model an Example CSS 902.4.4 Summary 982.5 An OpEM Example of Preemptive Scheduling 992.6 Summary 112Problems 114References 118Bibliography 1193 Systems Design and Model Development 1203.1 Inventory System 1223.1.1 Inventory System Model Development 1223.1.2 Inventory System Model Description 1253.1.3 Inventory System Model Operation 1323.1.4 Summary 1323.2 Part Production System 1343.2.1 Part Production System Model Development 1343.2.2 Part Production System Model Description 1373.2.3 Part Production System Model Operation 1413.3 Seaport System 1423.3.1 Seaport System Model Development 1423.3.2 Seaport System Model Description 1453.3.3 Seaport System Model Operation 1513.4 Advanced Features of OpEMCSS 1533.4.1 Expanded Split and Assemble Operation 1543.4.2 Preemption of a Resource 1673.4.3 “Wake Up” a Passivated Process 1723.5 Summary 172Problems 174References 1764 Markov Model Development 1774.1 Discrete-Time Markov Chains 1784.1.1 Stochastic Processes 1784.1.2 Transition Probabilities 1794.1.3 Properties of a Finite-State Markov Chain 1804.1.4 Development of [P]n 1814.1.5 Steady-State Solution 1824.1.6 First-Passage Times 1874.2 Continuous-Time Markov Processes 1894.2.1 Poisson Distribution 1894.2.2 Kolmogorov Differential Equations 1914.2.3 Transition Intensities for Poisson Process 1944.2.4 Transition Matrix for Several Examples 1964.2.5 Markov Process Model of a Queuing System 1994.2.6 Summary of Assumptions 2034.3 Semi-Markov Flow Graphs 2054.3.1 Definitions 2064.3.2 Laplace Transforms 2074.3.3 Flow-Graph Reduction 2104.3.4 Thief of Baghdad Process 2134.3.5 General Reaction Time Distributions 2154.3.6 Summary of Flow-Graph Techniques 2174.4 System Design and Evaluation Using Markov Models 2174.4.1 Data Communications System Design Problem 2174.4.2 Markov Model of Sequential Link Operation 2194.4.3 Markov Model of Parallel Link Operation 2224.4.4 Sensitivity of Link Effectiveness 2274.4.5 Conclusions 232Problems 234References 237Bibliography 2375 Reliability Processes 2385.1 Definitions 2385.1.1 System 2385.1.2 Multidimensional System Analysis 2395.1.3 Equipment Dependency Diagrams 2405.1.4 Reliability 2415.1.5 Reliability Process 2435.2 Reliability of Nonmaintained Module Groups 2445.2.1 Method 2445.2.2 Series Module Group 2455.2.3 Parallel Module Group 2465.2.4 Series–Parallel Module Group 2465.2.5 Four-Module Group 2475.2.6 Logic Techniques 2485.3 Availability of Maintained Module Groups 2495.3.1 Method 2495.3.2 Series Module Group 2495.3.3 Parallel Module Group 2525.3.4 Analysis of Maintained Module Groups 2535.4 Dependence of System Performance on Reliability 2535.4.1 System of Three Radars and Two Detection Consoles 2535.4.2 State-Space Equation 2545.4.3 Validation of Model Results 2565.4.4 Sensitivity Curve 2575.5 Summary 258Problems 258Bibliography 2606 Queuing Theory in Simulation-Based Systems Engineering 2616.1 Single-Queue, Single-Server Process 2626.1.1 Supermarket Checkout Stand 2626.1.2 Parallel Process 2636.1.3 Operational Sequence 2656.1.4 Finite Queue Model 2666.1.5 Infinite Queue Model 2716.1.6 Gamma Service Time 2746.2 Single-Queue, Two-Server Process 2756.2.1 Bank 2756.2.2 Parallel Process 2756.2.3 Operational Sequence 2776.2.4 Finite Queue Model 2786.2.5 Infinite Queue Model 2806.3 Comparison of Simulation, Markov Process, and Queuing Theory Models 281Problems 283Bibliography 2857 Rule-Based Learning and Adaptation 2867.1 Classifier Systems 2877.2 Induction of Decision-Making Rules 2897.2.1 Overview of the Rule Induction Problem 2897.2.2 Situational Universe for a Classifier System 2917.2.3 Lessons Learned from Previous Research 2937.2.4 Theory of Inductive Learning of Decision-Making Rules 2957.2.5 Summary of Induction Methods and Theory 2977.3 Supervisory Rule Learning 2977.3.1 Classifier Event Action Block 2977.3.2 Induction Process Model 3027.4 Generation of Planning Rules 3087.4.1 Prisoner’s Dilemma 3087.4.2 Finite-State Machine Model 3137.4.3 Grid World Model 3187.5 Summary 3207.6 Conclusions 322References 323Bibliography 3248 Agent Motion and Spatial Interactions 3258.1 Discrete-Event Model of Continuous Motion 3268.1.1 Range Closing/Range Not Closing Interaction 3268.1.2 Angle Closing/Angle Not Closing Interaction 3318.1.3 Intercept Interaction 3348.2 Agent Motion and Spatial Interaction Blocks 3358.2.1 Initialize Agent Event Action 3358.2.2 Change Agent Event Action 3368.2.3 Agent Interaction Event Action 3388.2.4 Animation Event Action 3428.3 World Model 3438.4 Sonar Array System 3548.5 Summary 366Bibliography 3689 Multiagent System of Systems 3699.1 Agents and Agent Interactions 3709.1.1 Agents 3709.1.2 Agent Interactions in System of Systems 3739.1.3 Bringing Multiagent Systems of Systems into Being 3759.2 Elevator System 3769.2.1 Person Arrival Process 3769.2.2 Person Process 3789.2.3 Elevator Motion Process 3799.2.4 Evaluation of Elevator System Performance 3829.3 Distributed, Vehicle Traffic Light Control System 3839.3.1 Traffic Control Agent 3849.3.2 Fuzzy Control 3879.3.3 Simulation of a Vehicle Traffic Control Network 3889.3.4 Results of Simulation Runs 3929.4 Communication Blocks for Multiagent Systems 3949.4.1 Memory Event Action Block 3949.4.2 Analysis Event Action Block 3979.4.3 Send Message Event Action Block 4009.4.4 Plan Execution Event Action Block 4019.4.5 Message Passing in a Multiagent System 4029.5 Summary 406References 408Bibliography 409Appendix A OpEMCSS User’s Manual 410A.1 Minimum Requirements for Successful CSS Modeling Languages 411A.2 Modeling Languages Survey 412A.2.1 Petri Nets 412A.2.2 IDEF0 Diagrams 412A.2.3 ExtendSim Queuing Models 413A.2.4 Modeling Languages Survey Summary 413A.3 Operational Evaluation Modeling (OpEM) Historical Overview 413A.4 OpEMCSS Familiarization Exercises 416A.4.1 How to Set Up ExtendSim LT-RunTime 416A.4.2 ExtendSim Environment Overview 418A.4.3 Block Familiarization Exercises 424A.5 Overview of Context-Sensitive Event Action Blocks 433A.5.1 Message Event Action Block 433A.5.2 Context-Sensitive Event Action Block 434A.5.3 Event Action Block 434A.6 Summary 434References 435Appendix B Overview of OpEMCSS Library Blocks 436B.1 Definition of OpEMCSS Block Categories 436B.2 Description of OpEMCSS Blocks by Category 437B.2.1 Category 1 437B.2.2 Category 2 439B.2.3 Category 3 441B.2.4 Category 4 444B.2.5 Category 5 454B.2.6 Category 6 464B.2.7 Category 7 469B.2.8 Category 8 473B.2.9 Category 9 475B.3 Summary of OpEMCSS Block Categories 476Appendix C Programming OpEMCSS Special Blocks 477C.1 Special Event Action Block Dialogs 478C.2 Execute Event Action Procedure 478C.3 Summary 484Index 487

John R. Clymer, PhD, is a Professor at California State University at Fullerton.

"Simulation-Based Engineering of Complex Systems provides a methodology and a tool to gain understanding on complex systems through simulation based engineering. The software tool (ExtendSimTM) which is provided as a CD works with the OpEMCSS library and gives the readers an opportunity of experimentation." (Journal of Artificial Societies and Social Simulation, July 2010)

• Preface xiiiAcknowledgments xviiOverview xix1 Introduction to Simulation-Based Systems Engineering 11.1 Definition of Complex Systems 31.1.1 Exercise: Model a Goal-Oriented Activity 61.1.2 Agent-Based System Architectures 91.1.3 Simulation and AI-Based System Design 111.1.4 Expansionism Versus Reductionism 121.1.5 Summary 151.2 Using Simulation to Understand Complex Systems 151.2.1 ExtendSim Discrete-Event Simulation User Environment and OpEMCSS Overview 151.2.2 Simulation Model Development Procedure 171.2.3 Simulation Programs: How Serial and Parallel Process Models Work 211.2.4 Sensitivity Analysis 291.3 Bringing Complex Systems into Being 301.3.1 Definition of Systems Engineering 311.3.2 Levels of System Description 331.3.3 Systems Engineering Life Cycle 351.3.4 Simulation of the System Development Process 381.3.5 Simulation-Based Systems Engineering 461.4 Summary 47Problems 50References 53Bibliography 532 Simulation Concepts and Building Blocks 552.1 Statistical Aspects of Simulation 562.1.1 Convergence Theorems 572.1.2 Uniform Random-Number Generator 582.1.3 Discrete Probability Distributions 592.1.4 Goodness-of-Fit Test 602.1.5 Generation of Random Variables 622.2 OpEM Graphical Modeling Language 642.2.1 Petri Nets 652.2.2 OpEM Graphs 682.3 OpEM Parallel Process Simulations 722.3.1 Sequential Process Event 762.3.2 Split Event 782.3.3 Complex Assemble Event 802.3.4 Simple Assemble Event 832.3.5 Comparison of Petri Nets and OpEM Graphs 842.4 OpEMCSS Simulation of Context-Sensitive Systems 862.4.1 Types of CSS Process Interactions and Timeline Analysis 862.4.2 How ExtendSim Has Been Modified to Implement the OpEM Language 882.4.3 How OpEMCSS Blocks Work Together to Model an Example CSS 902.4.4 Summary 982.5 An OpEM Example of Preemptive Scheduling 992.6 Summary 112Problems 114References 118Bibliography 1193 Systems Design and Model Development 1203.1 Inventory System 1223.1.1 Inventory System Model Development 1223.1.2 Inventory System Model Description 1253.1.3 Inventory System Model Operation 1323.1.4 Summary 1323.2 Part Production System 1343.2.1 Part Production System Model Development 1343.2.2 Part Production System Model Description 1373.2.3 Part Production System Model Operation 1413.3 Seaport System 1423.3.1 Seaport System Model Development 1423.3.2 Seaport System Model Description 1453.3.3 Seaport System Model Operation 1513.4 Advanced Features of OpEMCSS 1533.4.1 Expanded Split and Assemble Operation 1543.4.2 Preemption of a Resource 1673.4.3 “Wake Up” a Passivated Process 1723.5 Summary 172Problems 174References 1764 Markov Model Development 1774.1 Discrete-Time Markov Chains 1784.1.1 Stochastic Processes 1784.1.2 Transition Probabilities 1794.1.3 Properties of a Finite-State Markov Chain 1804.1.4 Development of [P]n 1814.1.5 Steady-State Solution 1824.1.6 First-Passage Times 1874.2 Continuous-Time Markov Processes 1894.2.1 Poisson Distribution 1894.2.2 Kolmogorov Differential Equations 1914.2.3 Transition Intensities for Poisson Process 1944.2.4 Transition Matrix for Several Examples 1964.2.5 Markov Process Model of a Queuing System 1994.2.6 Summary of Assumptions 2034.3 Semi-Markov Flow Graphs 2054.3.1 Definitions 2064.3.2 Laplace Transforms 2074.3.3 Flow-Graph Reduction 2104.3.4 Thief of Baghdad Process 2134.3.5 General Reaction Time Distributions 2154.3.6 Summary of Flow-Graph Techniques 2174.4 System Design and Evaluation Using Markov Models 2174.4.1 Data Communications System Design Problem 2174.4.2 Markov Model of Sequential Link Operation 2194.4.3 Markov Model of Parallel Link Operation 2224.4.4 Sensitivity of Link Effectiveness 2274.4.5 Conclusions 232Problems 234References 237Bibliography 2375 Reliability Processes 2385.1 Definitions 2385.1.1 System 2385.1.2 Multidimensional System Analysis 2395.1.3 Equipment Dependency Diagrams 2405.1.4 Reliability 2415.1.5 Reliability Process 2435.2 Reliability of Nonmaintained Module Groups 2445.2.1 Method 2445.2.2 Series Module Group 2455.2.3 Parallel Module Group 2465.2.4 Series–Parallel Module Group 2465.2.5 Four-Module Group 2475.2.6 Logic Techniques 2485.3 Availability of Maintained Module Groups 2495.3.1 Method 2495.3.2 Series Module Group 2495.3.3 Parallel Module Group 2525.3.4 Analysis of Maintained Module Groups 2535.4 Dependence of System Performance on Reliability 2535.4.1 System of Three Radars and Two Detection Consoles 2535.4.2 State-Space Equation 2545.4.3 Validation of Model Results 2565.4.4 Sensitivity Curve 2575.5 Summary 258Problems 258Bibliography 2606 Queuing Theory in Simulation-Based Systems Engineering 2616.1 Single-Queue, Single-Server Process 2626.1.1 Supermarket Checkout Stand 2626.1.2 Parallel Process 2636.1.3 Operational Sequence 2656.1.4 Finite Queue Model 2666.1.5 Infinite Queue Model 2716.1.6 Gamma Service Time 2746.2 Single-Queue, Two-Server Process 2756.2.1 Bank 2756.2.2 Parallel Process 2756.2.3 Operational Sequence 2776.2.4 Finite Queue Model 2786.2.5 Infinite Queue Model 2806.3 Comparison of Simulation, Markov Process, and Queuing Theory Models 281Problems 283Bibliography 2857 Rule-Based Learning and Adaptation 2867.1 Classifier Systems 2877.2 Induction of Decision-Making Rules 2897.2.1 Overview of the Rule Induction Problem 2897.2.2 Situational Universe for a Classifier System 2917.2.3 Lessons Learned from Previous Research 2937.2.4 Theory of Inductive Learning of Decision-Making Rules 2957.2.5 Summary of Induction Methods and Theory 2977.3 Supervisory Rule Learning 2977.3.1 Classifier Event Action Block 2977.3.2 Induction Process Model 3027.4 Generation of Planning Rules 3087.4.1 Prisoner’s Dilemma 3087.4.2 Finite-State Machine Model 3137.4.3 Grid World Model 3187.5 Summary 3207.6 Conclusions 322References 323Bibliography 3248 Agent Motion and Spatial Interactions 3258.1 Discrete-Event Model of Continuous Motion 3268.1.1 Range Closing/Range Not Closing Interaction 3268.1.2 Angle Closing/Angle Not Closing Interaction 3318.1.3 Intercept Interaction 3348.2 Agent Motion and Spatial Interaction Blocks 3358.2.1 Initialize Agent Event Action 3358.2.2 Change Agent Event Action 3368.2.3 Agent Interaction Event Action 3388.2.4 Animation Event Action 3428.3 World Model 3438.4 Sonar Array System 3548.5 Summary 366Bibliography 3689 Multiagent System of Systems 3699.1 Agents and Agent Interactions 3709.1.1 Agents 3709.1.2 Agent Interactions in System of Systems 3739.1.3 Bringing Multiagent Systems of Systems into Being 3759.2 Elevator System 3769.2.1 Person Arrival Process 3769.2.2 Person Process 3789.2.3 Elevator Motion Process 3799.2.4 Evaluation of Elevator System Performance 3829.3 Distributed, Vehicle Traffic Light Control System 3839.3.1 Traffic Control Agent 3849.3.2 Fuzzy Control 3879.3.3 Simulation of a Vehicle Traffic Control Network 3889.3.4 Results of Simulation Runs 3929.4 Communication Blocks for Multiagent Systems 3949.4.1 Memory Event Action Block 3949.4.2 Analysis Event Action Block 3979.4.3 Send Message Event Action Block 4009.4.4 Plan Execution Event Action Block 4019.4.5 Message Passing in a Multiagent System 4029.5 Summary 406References 408Bibliography 409Appendix A OpEMCSS User’s Manual 410A.1 Minimum Requirements for Successful CSS Modeling Languages 411A.2 Modeling Languages Survey 412A.2.1 Petri Nets 412A.2.2 IDEF0 Diagrams 412A.2.3 ExtendSim Queuing Models 413A.2.4 Modeling Languages Survey Summary 413A.3 Operational Evaluation Modeling (OpEM) Historical Overview 413A.4 OpEMCSS Familiarization Exercises 416A.4.1 How to Set Up ExtendSim LT-RunTime 416A.4.2 ExtendSim Environment Overview 418A.4.3 Block Familiarization Exercises 424A.5 Overview of Context-Sensitive Event Action Blocks 433A.5.1 Message Event Action Block 433A.5.2 Context-Sensitive Event Action Block 434A.5.3 Event Action Block 434A.6 Summary 434References 435Appendix B Overview of OpEMCSS Library Blocks 436B.1 Definition of OpEMCSS Block Categories 436B.2 Description of OpEMCSS Blocks by Category 437B.2.1 Category 1 437B.2.2 Category 2 439B.2.3 Category 3 441B.2.4 Category 4 444B.2.5 Category 5 454B.2.6 Category 6 464B.2.7 Category 7 469B.2.8 Category 8 473B.2.9 Category 9 475B.3 Summary of OpEMCSS Block Categories 476Appendix C Programming OpEMCSS Special Blocks 477C.1 Special Event Action Block Dialogs 478C.2 Execute Event Action Procedure 478C.3 Summary 484Index 487

John R. Clymer, PhD, is a Professor at California State University at Fullerton.

"Simulation-Based Engineering of Complex Systems provides a methodology and a tool to gain understanding on complex systems through simulation based engineering. The software tool (ExtendSimTM) which is provided as a CD works with the OpEMCSS library and gives the readers an opportunity of experimentation." (Journal of Artificial Societies and Social Simulation, July 2010)

• Preface xiiiAcknowledgments xviiOverview xix1 Introduction to Simulation-Based Systems Engineering 11.1 Definition of Complex Systems 31.1.1 Exercise: Model a Goal-Oriented Activity 61.1.2 Agent-Based System Architectures 91.1.3 Simulation and AI-Based System Design 111.1.4 Expansionism Versus Reductionism 121.1.5 Summary 151.2 Using Simulation to Understand Complex Systems 151.2.1 ExtendSim Discrete-Event Simulation User Environment and OpEMCSS Overview 151.2.2 Simulation Model Development Procedure 171.2.3 Simulation Programs: How Serial and Parallel Process Models Work 211.2.4 Sensitivity Analysis 291.3 Bringing Complex Systems into Being 301.3.1 Definition of Systems Engineering 311.3.2 Levels of System Description 331.3.3 Systems Engineering Life Cycle 351.3.4 Simulation of the System Development Process 381.3.5 Simulation-Based Systems Engineering 461.4 Summary 47Problems 50References 53Bibliography 532 Simulation Concepts and Building Blocks 552.1 Statistical Aspects of Simulation 562.1.1 Convergence Theorems 572.1.2 Uniform Random-Number Generator 582.1.3 Discrete Probability Distributions 592.1.4 Goodness-of-Fit Test 602.1.5 Generation of Random Variables 622.2 OpEM Graphical Modeling Language 642.2.1 Petri Nets 652.2.2 OpEM Graphs 682.3 OpEM Parallel Process Simulations 722.3.1 Sequential Process Event 762.3.2 Split Event 782.3.3 Complex Assemble Event 802.3.4 Simple Assemble Event 832.3.5 Comparison of Petri Nets and OpEM Graphs 842.4 OpEMCSS Simulation of Context-Sensitive Systems 862.4.1 Types of CSS Process Interactions and Timeline Analysis 862.4.2 How ExtendSim Has Been Modified to Implement the OpEM Language 882.4.3 How OpEMCSS Blocks Work Together to Model an Example CSS 902.4.4 Summary 982.5 An OpEM Example of Preemptive Scheduling 992.6 Summary 112Problems 114References 118Bibliography 1193 Systems Design and Model Development 1203.1 Inventory System 1223.1.1 Inventory System Model Development 1223.1.2 Inventory System Model Description 1253.1.3 Inventory System Model Operation 1323.1.4 Summary 1323.2 Part Production System 1343.2.1 Part Production System Model Development 1343.2.2 Part Production System Model Description 1373.2.3 Part Production System Model Operation 1413.3 Seaport System 1423.3.1 Seaport System Model Development 1423.3.2 Seaport System Model Description 1453.3.3 Seaport System Model Operation 1513.4 Advanced Features of OpEMCSS 1533.4.1 Expanded Split and Assemble Operation 1543.4.2 Preemption of a Resource 1673.4.3 “Wake Up” a Passivated Process 1723.5 Summary 172Problems 174References 1764 Markov Model Development 1774.1 Discrete-Time Markov Chains 1784.1.1 Stochastic Processes 1784.1.2 Transition Probabilities 1794.1.3 Properties of a Finite-State Markov Chain 1804.1.4 Development of [P]n 1814.1.5 Steady-State Solution 1824.1.6 First-Passage Times 1874.2 Continuous-Time Markov Processes 1894.2.1 Poisson Distribution 1894.2.2 Kolmogorov Differential Equations 1914.2.3 Transition Intensities for Poisson Process 1944.2.4 Transition Matrix for Several Examples 1964.2.5 Markov Process Model of a Queuing System 1994.2.6 Summary of Assumptions 2034.3 Semi-Markov Flow Graphs 2054.3.1 Definitions 2064.3.2 Laplace Transforms 2074.3.3 Flow-Graph Reduction 2104.3.4 Thief of Baghdad Process 2134.3.5 General Reaction Time Distributions 2154.3.6 Summary of Flow-Graph Techniques 2174.4 System Design and Evaluation Using Markov Models 2174.4.1 Data Communications System Design Problem 2174.4.2 Markov Model of Sequential Link Operation 2194.4.3 Markov Model of Parallel Link Operation 2224.4.4 Sensitivity of Link Effectiveness 2274.4.5 Conclusions 232Problems 234References 237Bibliography 2375 Reliability Processes 2385.1 Definitions 2385.1.1 System 2385.1.2 Multidimensional System Analysis 2395.1.3 Equipment Dependency Diagrams 2405.1.4 Reliability 2415.1.5 Reliability Process 2435.2 Reliability of Nonmaintained Module Groups 2445.2.1 Method 2445.2.2 Series Module Group 2455.2.3 Parallel Module Group 2465.2.4 Series–Parallel Module Group 2465.2.5 Four-Module Group 2475.2.6 Logic Techniques 2485.3 Availability of Maintained Module Groups 2495.3.1 Method 2495.3.2 Series Module Group 2495.3.3 Parallel Module Group 2525.3.4 Analysis of Maintained Module Groups 2535.4 Dependence of System Performance on Reliability 2535.4.1 System of Three Radars and Two Detection Consoles 2535.4.2 State-Space Equation 2545.4.3 Validation of Model Results 2565.4.4 Sensitivity Curve 2575.5 Summary 258Problems 258Bibliography 2606 Queuing Theory in Simulation-Based Systems Engineering 2616.1 Single-Queue, Single-Server Process 2626.1.1 Supermarket Checkout Stand 2626.1.2 Parallel Process 2636.1.3 Operational Sequence 2656.1.4 Finite Queue Model 2666.1.5 Infinite Queue Model 2716.1.6 Gamma Service Time 2746.2 Single-Queue, Two-Server Process 2756.2.1 Bank 2756.2.2 Parallel Process 2756.2.3 Operational Sequence 2776.2.4 Finite Queue Model 2786.2.5 Infinite Queue Model 2806.3 Comparison of Simulation, Markov Process, and Queuing Theory Models 281Problems 283Bibliography 2857 Rule-Based Learning and Adaptation 2867.1 Classifier Systems 2877.2 Induction of Decision-Making Rules 2897.2.1 Overview of the Rule Induction Problem 2897.2.2 Situational Universe for a Classifier System 2917.2.3 Lessons Learned from Previous Research 2937.2.4 Theory of Inductive Learning of Decision-Making Rules 2957.2.5 Summary of Induction Methods and Theory 2977.3 Supervisory Rule Learning 2977.3.1 Classifier Event Action Block 2977.3.2 Induction Process Model 3027.4 Generation of Planning Rules 3087.4.1 Prisoner’s Dilemma 3087.4.2 Finite-State Machine Model 3137.4.3 Grid World Model 3187.5 Summary 3207.6 Conclusions 322References 323Bibliography 3248 Agent Motion and Spatial Interactions 3258.1 Discrete-Event Model of Continuous Motion 3268.1.1 Range Closing/Range Not Closing Interaction 3268.1.2 Angle Closing/Angle Not Closing Interaction 3318.1.3 Intercept Interaction 3348.2 Agent Motion and Spatial Interaction Blocks 3358.2.1 Initialize Agent Event Action 3358.2.2 Change Agent Event Action 3368.2.3 Agent Interaction Event Action 3388.2.4 Animation Event Action 3428.3 World Model 3438.4 Sonar Array System 3548.5 Summary 366Bibliography 3689 Multiagent System of Systems 3699.1 Agents and Agent Interactions 3709.1.1 Agents 3709.1.2 Agent Interactions in System of Systems 3739.1.3 Bringing Multiagent Systems of Systems into Being 3759.2 Elevator System 3769.2.1 Person Arrival Process 3769.2.2 Person Process 3789.2.3 Elevator Motion Process 3799.2.4 Evaluation of Elevator System Performance 3829.3 Distributed, Vehicle Traffic Light Control System 3839.3.1 Traffic Control Agent 3849.3.2 Fuzzy Control 3879.3.3 Simulation of a Vehicle Traffic Control Network 3889.3.4 Results of Simulation Runs 3929.4 Communication Blocks for Multiagent Systems 3949.4.1 Memory Event Action Block 3949.4.2 Analysis Event Action Block 3979.4.3 Send Message Event Action Block 4009.4.4 Plan Execution Event Action Block 4019.4.5 Message Passing in a Multiagent System 4029.5 Summary 406References 408Bibliography 409Appendix A OpEMCSS User’s Manual 410A.1 Minimum Requirements for Successful CSS Modeling Languages 411A.2 Modeling Languages Survey 412A.2.1 Petri Nets 412A.2.2 IDEF0 Diagrams 412A.2.3 ExtendSim Queuing Models 413A.2.4 Modeling Languages Survey Summary 413A.3 Operational Evaluation Modeling (OpEM) Historical Overview 413A.4 OpEMCSS Familiarization Exercises 416A.4.1 How to Set Up ExtendSim LT-RunTime 416A.4.2 ExtendSim Environment Overview 418A.4.3 Block Familiarization Exercises 424A.5 Overview of Context-Sensitive Event Action Blocks 433A.5.1 Message Event Action Block 433A.5.2 Context-Sensitive Event Action Block 434A.5.3 Event Action Block 434A.6 Summary 434References 435Appendix B Overview of OpEMCSS Library Blocks 436B.1 Definition of OpEMCSS Block Categories 436B.2 Description of OpEMCSS Blocks by Category 437B.2.1 Category 1 437B.2.2 Category 2 439B.2.3 Category 3 441B.2.4 Category 4 444B.2.5 Category 5 454B.2.6 Category 6 464B.2.7 Category 7 469B.2.8 Category 8 473B.2.9 Category 9 475B.3 Summary of OpEMCSS Block Categories 476Appendix C Programming OpEMCSS Special Blocks 477C.1 Special Event Action Block Dialogs 478C.2 Execute Event Action Procedure 478C.3 Summary 484Index 487