Agent Directed Simulation and Systems Engineering Wiley Series in Systems Engineering and Management 1st Edition by Levent Yilmaz, Tuncer Oren 9783527407811 3527407812

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ISBN 10: 3527407812
ISBN 13: 9783527407811
Author: Levent Yilmaz, Tuncer Oren

The only book to present the synergy between modeling and simulation, systems engineering, and agent technologies expands the notion of agent-based simulation to also deal with agent simulation and agent-supported simulation. Accessible to both practitioners and managers, it systematically addresses designing and building agent systems from a systems engineering perspective.

Agent Directed Simulation and Systems Engineering Wiley Series in Systems Engineering and Management 1st Table of contents:

Part One Background 

1 Modeling and Simulation: a Comprehensive and Integrative View 
Tuncer I. Ören

1.1 Introduction 

1.2 Simulation: Several Perspectives 

1.2.1 Purpose of Use 

1.2.2 Problem to Be Solved 

1.2.3 Connectivity of Operations 

1.2.4 M&S as a Type of Knowledge Processing 

1.2.5 M&S from the Perspective of Philosophy of Science 

1.3 Model-Based Activities 

1.3.1 Model Building 

1.3.2 Model-Base Management 

1.3.3 Model Processing 

1.3.4 Behavior Generation 

1.4 Synergies of M&S: Mutual and Higher-Order Contributions 

1.5 Advancement of M&S 

1.6 Preeminence of M&S 

1.6.1 Physical Tools 

1.6.2 Knowledge-Based or Soft Tools 

1.6.3 Knowledge Generation Tools 

1.7 Summary and Conclusions 

2 Autonomic Introspective Simulation Systems 
Levent Yilmaz and Bradley Mitchell

2.1 Introduction 

2.2 Perspective and Background on Autonomic Systems 

2.3 Decentralized Autonomic Simulation Systems: Prospects and Issues 

2.3.1 Motivating Scenario: Adaptive Experience Management in Distributed Mission Training 

2.3.2 An Architectural Framework for Decentralized Autonomic Simulation Systems 

2.3.3 Challenges and Issues 

2.4 Symbiotic Adaptive Multisimulation: An Autonomic Simulation System 

2.4.1 Metamodels for Introspection Layer Design 

2.4.2 Local Adaptation: First-Order Change via Particle Swarm Optimizer 

2.4.3 The Learning Layer: Genetic Search of Potential System Configurations 

2.4.4 SAMS Component Architecture

2.5 Case Study: UAV Search and Attack Scenario 

2.5.1 Input Factors 

2.5.2 Agent Specifications 

2.6 Validation and Preliminary Experimentation with SAMS 

2.6.1 Face Validity of the UAV Model 

2.6.2 Experiments with the Parallel SAMS Application 

2.7 Summary 

Part Two Agents and Modeling and Simulation 

3 Agents: Agenthood, Agent Architectures, and Agent Taxonomies 
Andreas Tolk and Adelinde M. Uhrmacher

3.1 Introduction 

3.2 Agenthood 

3.2.1 Defining Agents 

3.2.2 Situated Environment and Agent Society 

3.3 Agent Architectures 

3.3.1 Realizing Situatedness 

3.3.2 Realizing Autonomy

3.3.3 Realizing Flexibility

3.3.4 Architectures and Characteristics 

3.4 Agenthood Implications for Practical Applications 

3.4.1 Systems Engineering, Simulation, and Agents 

3.4.2 Modeling and Simulating Human Behavior for Systems Engineering 

3.4.3 Simulation-Based Testing in Systems Engineering 

3.4.4 Simulation as Support for Decision Making in Systems Engineering 

3.4.5 Implications for Modeling and Simulation Methods 

3.5 Agent Taxonomies 

3.5.1 History and Application-Specific Taxonomies 

3.5.2 Categorizing the Agent Space 

3.6 Concluding Discussion 

4 Agent-directed Simulation 
Levent Yilmaz and Tuncer I. Ören

4.1 Introduction 111

4.2 Background

4.2.1 Software Agents 

4.2.2 Complexity 

4.2.3 Complex Systems of Systems 

4.2.4 Software Agents within the Spectrum of Computational Paradigms 

4.3 Categorizing the Use of Agents in Simulation

4.3.1 Agent Simulation 

4.3.2 Agent-Based Simulation 

4.3.3 Agent-Supported Simulation 

4.4 Agent Simulation 

4.4.1 A Metamodel for Agent System Models 

4.4.2 A Taxonomy for Modeling Agent System Models 

4.4.3 Using Agents as Model Design Metaphors: Agent-Based Modeling 

4.4.4 Simulation of Agent Systems 

4.5 Agent-Based Simulation

4.5.1 Autonomic Introspective Simulation

4.5.2 Agent-Coordinated Simulator for Exploratory Multisimulation 

4.6 Agent-Supported Simulation

4.6.1 Agent-Mediated Interoperation of Simulations

4.6.2 Agent-Supported Simulation for Decision Support 

4.7 Summary 

Part Three Systems Engineering and Quality Assurance for Agent-Directed Simulation 

5 Systems Engineering: Basic Concepts and Life Cycle 
Steven M. Biemer and Andrew P. Sage

5.1 Introduction 

5.2 Agent-Based Systems Engineering 

5.3 Systems Engineering Definition and Attributes

5.3.1 Knowledge 

5.3.2 People and Information Management 

5.3.3 Processes 

5.3.4 Methods and Tools 

5.3.5 The Need for Systems Engineering 

5.4 The System Life Cycle 

5.4.1 Conceptual Design (Requirements Analysis) 

5.4.2 Preliminary Design (Systems Architecting) 

5.4.3 Detailed Design and Development 

5.4.4 Production and Construction 

5.4.5 Operational Use and System Support 

5.5 Key Concepts of Systems Engineering

5.5.1 Integrating Perspectives into the Whole 

5.5.2 Risk Management 

5.5.3 Decisions and Trade Studies (the Strength of Alternatives) 

5.5.4 Modeling and Evaluating the System 

5.6 Summary 

6 Quality Assurance of Simulation Studies of Complex Networked Agent Systems 
Osman Balci, William F. Ormsby, and Levent Yilmaz

6.1 Introduction 

6.2 Characteristics of Open Agent Systems

6.3 Issues in the Quality Assurance of Agent Simulations 

6.4 Large-Scale Open Complex Systems – The Network-Centric System Metaphor 

6.5 M&S Challenges for Large-Scale Open Complex Systems 

6.6 Quality Assessment of Simulations of Large-Scale Open Systems 

6.7 Conclusions

7 Failure Avoidance in Agent-directed Simulation: Beyond Conventional v&v and qa 
Tuncer I. Ören and Levent Yilmaz

7.1 Introduction 

7.1.1 The Need for a Fresh Look 

7.1.2 Basic Terms 

7.2 What Can Go Wrong 

7.2.1 Increasing Importance of M&S 

7.2.2 Contributions of Simulation to Failure Avoidance 

7.2.3 Need for Failure Avoidance in Simulation Studies 

7.2.4 Some Sources of Failure in M&S 

7.3 Assessment for M&S 

7.3.1 Types of Assessment 

7.3.2 Criteria for Assessment 

7.3.3 Elements of M&S to be Studied 

7.4 Need for Multiparadigm Approach for Successful M&S Projects 

7.4.1 V&V Paradigm for Successful M&S Projects 

7.4.2 QA Paradigm for Successful M&S Projects

7.4.3 Failure Avoidance Paradigm for Successful M&S Projects 

7.4.4 Lessons Learned and Best Practices for Successful M&S Projects 

7.5 Failure Avoidance for Agent-Based Modeling

7.5.1 Failure Avoidance in Rule-Based Systems 

7.5.2 Failure Avoidance in Autonomous Systems 

7.5.3 Failure Avoidance in Agents with Personality, Emotions, and Cultural Background

7.5.4 Failure Avoidance in Inputs 

7.6 Failure Avoidance for Systems Engineering 

7.7 Conclusion 

8 Toward Systems Engineering for Agent-directed Simulation 
Levent Yilmaz

8.1 Introduction 

8.2 What Is a System? 

8.2.1 What Is Systems Engineering? 

8.2.2 The Functions of Systems Engineering 

8.3 Modeling and Simulation 

8.4 The Synergy of M&S and SE 

8.4.1 The Role of M&S in Systems 

8.4.2 Why Does M&S Require SE?

8.4.3 Why Is SSE Necessary? 

8.5 Toward Systems Engineering for Agent-Directed Simulation 

8.5.1 The Essence of Complex Adaptive Open Systems (CAOS) 

8.5.2 The Merits of ADS 

8.5.3 Systems Engineering for Agent-Directed Simulation 

8.6 Sociocognitive Framework for ADS-SE 

8.6.1 Social-Cognitive View 

8.6.2 The Dimensions of Representation 

8.6.3 The Functions for Analysis 

8.7 Case Study: Human-Centered Work Systems 

8.7.1 Operational Level – Organizational Subsystem 

8.7.2 Operational Level – Organizational Subsystem 

8.7.3 Operational Level – Integration of Organization and Social Subsystems 

8.7.4 The Technical Level

8.8 Conclusions 

9 Design and Analysis of Organization Adaptation in Agent Systems 
Virginia Dignum, Frank Dignum, and Liz Sonenberg

9.1 Introduction

9.2 Organizational Model

9.3 Organizational Structure 

9.3.1 Organizational Structures in Organization Theory 

9.3.2 Organizational Structures in Multiagent Systems 

9.4 Organization and Environment 

9.4.1 Environment Characteristics 

9.4.2 Congruence 

9.5 Organization and Autonomy 

9.6 Reorganization 

9.6.1 Organizational Utility 

9.6.2 Organizational Change 

9.7 Organizational Design 

9.7.1 Designing Organizational Simulations 

9.7.2 Application Scenario

9.8 Understanding Simulation of Reorganization 

9.8.1 Reorganization Dimensions 

9.8.2 Analyzing Simulation Case Studies 

9.9 Conclusions

10 Programming Languages, Environments, and Tools for Agent-directed Simulation 
Yu Zhang, Mark Lewis, and Maarten Sierhuis

10.1 Introduction 

10.2 Architectural Style for ADS

10.3 Agent-Directed Simulation – An Overview 

10.3.1 Language 

10.3.2 Environment 

10.3.3 Service 

10.3.4 Application 

10.4 A Survey of Five ADS Platforms 

10.4.1 Ascape 

10.4.2 NetLogo 

10.4.3 Repast 

10.4.4 Swarm 

10.4.5 Mason 

10.5 Brahms – A Multiagent Simulation for Work System Analysis and Design 

10.5.1 Language 

10.5.2 Environment

10.5.3 Service 

10.5.4 Application 

10.6 CASESim – A Multiagent Simulation for Cognitive Agents for Social Environment 

10.6.1 Language 

10.6.2 Environment 

10.6.3 Service 

10.6.4 Application 

10.7 Conclusion 

11 Simulation for Systems Engineering 
Joachim Fuchs

11.1 Introduction 

11.2 The Systems Engineering Process 

11.3 Modeling and Simulation Support 

11.4 Facilities 

11.5 An Industrial Use Case: Space Systems 

11.5.1 Simulators for Analysis and Design 

11.5.2 Facility for Spacecraft Qualification and Acceptance 

11.5.3 Facility for Ground System Qualification and Testing and Operations

11.6 Outlook 

11.7 Conclusions

12 Agent-directed Simulation for Systems Engineering 
Philip S. Barry, Matthew T.K. Koehler, and Brian F. Tivnan

12.1 Introduction 

12.2 New Approaches Are Needed 

12.2.1 Employing ADS Through the Framework of Empirical Relevance 

12.2.2 Simulating Systems of Systems 

12.3 Agent-Directed Simulation for the Systems Engineering of Human Complex Systems 

12.3.1 A Call for Agents in the Study of Human Complex Systems 

12.3.2 Noteworthy Agent-Directed Simulations in the Science of Human Complex Systems 

12.4 A Model-Centered Science of Human Complex Systems

12.5 An Infrastructure for the Engineering of Human Complex Systems 

12.5.1 Components of the Infrastructure for Complex Systems Engineering 

12.5.2 Modeling Goodness 

12.5.3 The Genetic Algorithm Optimization Toolkit 

12.6 Case Studies

12.6.1 Case Study 1: Defending The Stadium

12.6.2 Case Study 2: Secondary Effects from Pandemic Influenza 

12.7 Summary 

Part Four Agent-Directed Simulation for Systems Engineering 

13 Agent-implemented Experimental Frames for Net-centric Systems Test and Evaluation 
Bernard P. Zeigler, Dane Hall, and Manuel Salas

13.1 Introduction 

13.2 The Need for Verification Requirements 

13.3 Experimental Frames and System Entity Structures 

13.4 Decomposition and Design of System Architecture 

13.5 Employing Agents in M&S-Based Design, Verification and Validation 

13.6 Experimental Frame Concepts for Agent Implementation 

13.7 Agent-Implemented Experimental Frames 

13.8 DEVS/SOA: Net-Centric Execution Using Simulation Service

13.8.1 Automation of Agent Attachment to System Components 

13.8.2 DEVS-Agent Communications/Coordination

13.8.3 DEVS-Agent EndomorphicModels 

13.9 Summary and Conclusions 

13.A cAutoDEVS – A Tool for the Bifurcated Methodology 

14 Agents and Decision Support Systems 
Andreas Tolk, Poornima Madhavan, Jeffrey W. Tweedale, and Lakhmi C. Jain

14.1 Introduction 

14.1.1 History

14.1.2 Motivating Agent-Directed Decision Support Simulation Systems 

14.1.3 Working Definitions 

14.2 Cognitive Foundations for Decision Support 

14.2.1 Decision Support Systems as Social Actors 

14.2.2 How to Present the System to the User and Improve Trust

14.2.3 Relevance for the Engineer 

14.3 Technical Foundations for Decision Support 

14.3.1 Machine-Based Understanding for Decision Support 

14.3.2 Requirements for Systems When Being Used for Decision Support 

14.3.3 Agent-Directed Multimodel and Multisimulation Support

14.3.4 Methods Applicable to Support Agent-Directed Decision Support Simulation Systems 

14.4 Examples for Intelligent and Agent-Directed Decision Support Simulation Systems 

14.4.1 Supporting Command and Control 

14.4.2 Supporting Inventory Control and Integrated Logistics 

14.5 Conclusion

15 Agent Simulation for Software Process Performance Analysis 
Levent Yilmaz and Jared Phillips

15.1 Introduction 

15.2 Related Work 

15.2.1 Organization-Theoretic Perspective for Simulation-Based Analysis of Software Processes 

15.2.2 Simulation Methods for Software Process Performance Analysis 

15.3 Team-RUP: A Framework for Agent Simulation of Software Development Organizations 

15.3.1 Organization Structure 

15.3.2 Team-RUP Task Model 

15.3.3 Team-RUP Team Archetypes and Cooperation Mechanisms 

15.3.4 Reward Mechanism in Team-RUP 

15.4 Design and Implementation of Team-RUP 

15.4.1 Performance Metrics 

15.4.2 Validation of the Model 

15.5 Results and Discussion 

15.6 Conclusions 

16 Agent-Directed Simulation for Manufacturing System Engineering 
Jeffrey S. Smith, Erdal Sahin, and Levent Yilmaz

16.1 Introduction

16.1.1 Manufacturing Systems 

16.1.2 Agent-Based Modeling 

16.2 Simulation Modeling and Analysis for Manufacturing Systems

16.2.1 Manufacturing System Design 

16.2.2 Manufacturing Operation

16.3 Agent-Directed Simulation for Manufacturing Systems 

16.3.1 Emergent Approaches 

16.3.2 Agent-Based Manufacturing 

16.3.3 The Holonic Approach: Hierarchic Open Agent Systems 

16.4 Summary 

17 Organization and Work Systems Design and Engineering: from Simulation to Implementation of Multiagent Systems 
Maarten Sierhuis,William J. Clancey, and Chin H. Seah

17.1 Introduction

17.2 Work Systems Design

17.2.1 Existing Work System Design Methods

17.2.2 A Brief History of Work Systems Design 

17.3 Modeling and Simulation of Work Systems 

17.3.1 Designing Work Systems: What Is the Purpose and What Can Go Wrong? 

17.3.2 The Difficulty of Convincing Management 

17.4 Work Practice Modeling and Simulation 

17.4.1 Practice vs. Process 

17.4.2 Modeling Work Practice 

17.5 The Brahms Language 

17.5.1 Simulation or Execution with Brahms

17.5.2 Modeling People and Organizations 

17.5.3 Modeling Artifacts and Data Objects 

17.5.4 Modeling Communication

17.5.5 Modeling Location and Movement 

17.5.6 Java Integration

17.6 Systems Engineering: From Simulation to Implementation 

17.6.1 A Cyclic Approach 

17.6.2 Modeling Current Operations 

17.6.3 Modeling Future Operations 

17.6.4 MAS Implementation

17.7 A Case Study: The OCA Mirroring System

17.7.1 Mission Control as a Socio-Technical Work System 

17.7.2 The OCA Officer’s Work System 

17.7.3 Simulating the Current OCA Work System 

17.7.4 Designing the Future OCA Work System 

17.7.5 Simulating the Future OCA Work System

17.7.6 Implementing OCAMS 

17.8 Conclusion

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Agent Directed,Systems Engineering,Levent Yilmaz,Tuncer Oren