Adelinde M. Uhrmacher, Danny Weyns9781420070231, 1420070231
The intersection of agents, modeling, simulation, and application domains has been the subject of active research for over two decades. Although agents and simulation have been used effectively in a variety of application domains, much of the supporting research remains scattered in the literature, too often leaving scientists to develop multi-agent system (MAS) models and simulations from scratch.
Multi-Agent Systems: Simulation and Applications provides an overdue review of the wide ranging facets of MAS simulation, including methodological and application-oriented guidelines. This comprehensive resource reviews two decades of research in the intersection of MAS, simulation, and different application domains. It provides scientists and developers with disciplined engineering approaches to modeling and developing MAS-based simulations. After providing an overview of the field’s history and its basic principles, as well as cataloging the various simulation engines for MAS, the book devotes three sections to current and emerging approaches and applications.
Simulation for MAS — explains simulation support for agent decision making, the use of simulation for the design of self-organizing systems, the role of software architecture in simulating MAS, and the use of simulation for studying learning and stigmergic interaction.
MAS for Simulation — discusses an agent-based framework for symbiotic simulation, the use of country databases and expert systems for agent-based modeling of social systems, crowd-behavior modeling, agent-based modeling and simulation of adult stem cells, and agents for traffic simulation.
Tools — presents a number of representative platforms and tools for MAS and simulation, including Jason, James II, SeSAm, and RoboCup Rescue.
Complete with over 200 figures and formulas, this reference book provides the necessary overview of experiences with MAS simulation and the tools needed to exploit simulation in MAS for future research in a vast array of applications including home security, computational systems biology, and traffic management.
Table of contents :
Multi-Agent Systems: Simulation and Applications……Page 3
Contents……Page 5
Key Aims……Page 7
Structure of the Book……Page 8
Acknowledgments……Page 9
About the Editors……Page 10
Contributors……Page 11
Table of Contents……Page 0
Part I: Background……Page 13
1.1 Introduction……Page 15
1.2.1 The CNET Simulator……Page 17
1.2.2 The DVMT Project……Page 18
1.3.1 The Need for Individual-Based Modeling……Page 19
1.3.3 The Agent-Based Modeling Approach……Page 21
1.3.4 Agent-Based Social Simulation: Simulating Human-Inspired Behaviors……Page 23
The Reynolds’s Boids……Page 24
1.4.1 Agent……Page 25
Agent Architectures……Page 26
Modeling the Behavior of Agents……Page 28
An Essential Compound of MAS……Page 29
Modeling the Environment……Page 30
Communication by Message Passing……Page 32
Interactions Using Signals……Page 33
1.4.4 Modeling Time……Page 34
Modeling the Temporal Evolution of the Environment……Page 35
Coupling the Agents and the Environment: Scheduling the MAS……Page 36
1.4.5 Simulating MAS as Three Correlated Modeling Activities……Page 38
1.4.6 A Still-Incomplete Picture……Page 40
1.5.3 Model……Page 41
1.5.6 Simulation Relation: Simulator Correctness……Page 42
1.5.7 Deriving Three Fundamental Questions……Page 43
Identifying the Nature of the Source System……Page 44
Environment Module Issues: The Need for Virtual Reality……Page 45
Behavior Module Issues: Toward the Participatory Design of Simulation……Page 47
Merging Participatory Design and VR: A Promising Future for MABS……Page 48
MABS are Supposed to Model MAS……Page 49
Toward Paradigmatic Validity: The Need of Linking the Micro and Macro Levels……Page 50
The Fundamental Problem of Replication……Page 51
1.7 Conclusion……Page 53
References……Page 54
2.1 Simulation in the Sciences of Complex Systems……Page 64
2.2 Predecessors and Alternatives……Page 67
2.2.2 Dynamic Microsimulation to Predict Demographic Processes……Page 68
2.2.3 Cellular Automata: Simple Agents Moving on a Topography……Page 69
2.2.4 Discrete Event Simulation……Page 73
2.2.5 Similarities and Differences Among These Approaches……Page 74
2.3 Unfolding, Nesting, Coping with Complexity……Page 75
2.3.1 Agents with Different Roles in Different Environments……Page 76
2.3.3 The Role of the Environment……Page 78
2.4.1 Agent Communication……Page 80
2.4.2 Concluding Remarks……Page 81
References……Page 82
3.1 Introduction……Page 87
3.2 Multi-Agent System Architectures……Page 88
3.3.2 A Survey of MAS Simulation Toolkits……Page 89
JAMES II……Page 90
RePast……Page 91
Swarm……Page 93
CHARON……Page 94
3.3.3 Taxonomy of Discrete Event Simulation Toolkits……Page 95
3.4.1 Parallel Discrete Event Simulation……Page 96
Gensim and DGensim……Page 97
HLA Agent……Page 99
HLA RePast……Page 100
HLA JADE……Page 102
JAMES II……Page 103
SPADES……Page 104
Charon……Page 105
3.5.1 Scalability of Parallel Engines……Page 106
Shared State and Data Distribution……Page 107
Instrumentation and visualization for MAS……Page 108
References……Page 109
Part II: Simulation for MAS……Page 116
4.1 Introduction……Page 118
4.2.1 A Challenge for Simulation-Based Decision Making……Page 120
Environmentally-Mediated Interactions……Page 121
4.2.3 The Architecture……Page 122
Ghosts……Page 123
4.2.4 The Environment……Page 124
Polyagents and Multi-Agent Paradigms……Page 125
Polyagents and Simulation……Page 126
4.3.1 Factory Scheduling and Control……Page 127
4.3.2 Vehicle Routing……Page 128
4.3.3 Prediction……Page 131
4.4.1 Theoretical Opportunities……Page 133
4.4.2 New Applications……Page 135
4.5 Conclusion……Page 136
References……Page 137
5.1 Introduction……Page 141
5.2.1 The Role of Environment in Self-Organizing Systems……Page 144
5.2.2 Overview of the A&A Meta-Model……Page 145
5.2.3 An Architectural Pattern……Page 146
5.3 Methodological Issues Raised by Self-Organizing Systems……Page 147
5.4.1 Overview……Page 148
5.4.2 Modeling……Page 149
5.4.3 Simulation……Page 150
5.4.5 Tuning……Page 151
5.5.2 Simulation……Page 152
5.6 Case Study: Plain Diffusion……Page 153
5.6.1 Problem Statement……Page 154
5.6.2 Modeling Plain Diffusion……Page 155
5.6.3 Simulating Plain Diffusion……Page 156
5.6.4 Verifying Plain Diffusion……Page 158
5.6.5 Tuning Plain Diffusion……Page 162
5.7 Conclusion……Page 166
References……Page 169
6.1 Introduction……Page 174
6.2.1 A System and Its Environment……Page 176
6.2.2 Characteristics of Multi-Agent Control Systems……Page 177
6.3 AGV Transportation System……Page 179
Functionalities of an AGV Control System……Page 180
AGV Steering System……Page 182
6.3.3 Requirements of an AGV Simulator……Page 183
Structure of the Environment…….Page 184
Dynamism in the Environment…….Page 185
Modeling the Software of a Multi-Agent Control System……Page 186
Structure of the Warehouse Environment…….Page 188
Dynamism in the Warehouse Environment…….Page 189
Simulation Model for Integrating the AGV Agent Software……Page 190
Control Interface of AGV Agents…….Page 191
6.5 Architecture of the Simulation Platform……Page 192
6.5.1 Requirements……Page 193
Elements and Their Properties……Page 194
Low Coupling between Simulated Environment and Simulation Engine…….Page 196
Elements and Their Properties……Page 197
Low Coupling Due to Data Repositories…….Page 200
Elements and Their Properties……Page 201
The Simulation Engine Encapsulates All Synchronization…….Page 203
Aspect-Oriented Programming……Page 204
Flexibility of Embedding a Multi-Agent Control System…….Page 205
Elements and Their Properties……Page 206
6.6 Evaluating the AGV Simulator……Page 208
Setup of the Experiments……Page 209
Measurements……Page 210
6.6.3 Multi-Agent System Development Supported by the AGV Simulator……Page 211
6.7.1 Special-Purpose Simulation Platforms……Page 212
Measurement of Execution Time……Page 214
Specification of Execution Time……Page 215
Extending the Modeling Framework…….Page 216
6.8.2 Closing Reflection……Page 217
References……Page 218
7.1 Introduction……Page 222
Normal Form Games……Page 224
Categorization and Examples……Page 225
Pareto Optimality……Page 227
Evolutionary Stable Strategies……Page 228
7.2.2 Strategic Games with Continuous Strategy Spaces……Page 229
Discrete Time Replicator Dynamics……Page 231
Multi-Population Replicator Dynamics……Page 232
7.3.2 Replicator Dynamics in Continuous Strategy Spaces……Page 233
7.4.1 Analysis of the Evolutionary Dynamics of the Categorization of Games……Page 235
The Dynamics of Q-Learning in Games……Page 237
The Q-Learning Experiments……Page 238
7.5.1 Mutation as Engine for Diffusion in Continuous Strategy Spaces……Page 239
7.5.3 Draining of Pay-Off Streams in Strategy Space……Page 241
7.6 Example of the Resulting Dynamics of the Continuous Replicator Equations……Page 244
References……Page 247
8.1 Introduction……Page 249
8.1.1 Overview……Page 250
8.2.1 A Tale of a Wrong Story……Page 251
8.2.3 Stigmergic Cues as Practical Behavioral Traces……Page 253
8.3.1 Coordination and Cues of Interference……Page 254
8.3.2 Communication and Signals……Page 255
8.3.4 Stigmergic Self-Adjustment and Stigmergic Communication……Page 256
8.4.1 Cooperative and Competitive Interference……Page 257
8.5.1 Pheromones Are Not Stigmergic Cues……Page 258
8.6 Understanding Stigmergy through Evolution……Page 259
8.6.1 The Basic Model……Page 260
8.6.2 Evolution of Practical Behavior……Page 262
8.6.3 Evolution of Stigmergic Self-Adjustment and Indirect Coordination……Page 263
8.6.4 Evolution of Stigmergic Communication……Page 264
8.7 Future Work……Page 266
8.8 Conclusion……Page 267
References……Page 268
Part III: MAS for Simulation……Page 272
9.1 Introduction……Page 275
9.2 Cognitive Agent Modeling……Page 278
9.2.1 Major PMF Models within Each PMFserv Subsystem……Page 279
9.3 Social Agents, Factions, and the FactionSim Testbed……Page 282
9.4 Overview of Some Existing Country Databases……Page 286
9.5 Overview of Automated Data Extraction Technology……Page 288
9.6 Overview of Subject Matter Expert Studies/Surveys……Page 293
9.7 Overview of Integrative Knowledge Engineering Process……Page 294
9.8 Concluding Remarks……Page 300
Acknowledgment……Page 301
References……Page 302
10.1 Introduction……Page 305
10.2.1 Pedestrians as Particles……Page 307
10.2.2 Pedestrians as States of CA……Page 308
10.2.3 Pedestrians as Autonomous Agents……Page 309
10.3 Guidelines for Crowds Modeling with Situated Cellular Agents Approach……Page 310
10.3.1 Spatial Infrastructure and Active Elements of the Environment……Page 312
10.3.2 Pedestrians……Page 313
10.4.1 The Scenario……Page 314
10.4.2 The Modeling Assumptions……Page 315
10.4.4 The Passengers……Page 316
10.4.5 Simulation Results……Page 317
10.5 From a SCA Model to Its Implementation……Page 319
10.5.1 Supporting and Executing SCA Models……Page 320
10.6 Conclusions……Page 323
References……Page 325
11.1 Introduction……Page 329
11.1.1 Aim and Overview……Page 330
11.2.1 Macroscopic vs. Microscopic Approaches……Page 331
11.2.2 Driver-Vehicle Agents……Page 333
11.3.1 The Intelligent Driver Model……Page 335
11.3.2 Inter-Driver Variability……Page 339
11.3.3 Intra-Driver Variability……Page 341
11.4.1 Modeling Lane Changes……Page 342
11.4.2 Approaching a Traffic Light……Page 343
11.5 Microscopic Traffic Simulation Software……Page 345
11.5.2 Numerical Integration……Page 347
11.5.3 Visualization……Page 348
11.6.1 Emergence of Stop-and-Go Waves……Page 350
11.6.2 Impact of a Speed Limit……Page 351
11.6.3 Store-and-Forward Strategy for Inter-Vehicle Communication……Page 353
References……Page 356
12.1 Introduction……Page 361
12.2 Concepts of Symbiotic Simulation……Page 363
12.3 Different Classes of Symbiotic Simulation Systems……Page 365
12.3.2 Symbiotic Simulation Control Systems (SSCS)……Page 366
12.3.4 Symbiotic Simulation Model Validation Systems (SSMVS)……Page 367
12.3.6 Hybrid Symbiotic Simulation Systems……Page 368
12.4.1 Workflows……Page 369
SSCS Workflow……Page 370
SSFS Workflow……Page 371
SSADS Workflow……Page 372
Evaluate Trigger Conditions……Page 373
Create Scenario……Page 374
Analyze Results……Page 375
12.5.1 Architecture Requirements……Page 376
12.5.2 Discussion of Existing Architectures……Page 377
12.5.3 Web Services Approach vs. Agent-Based Approach……Page 378
12.5.4 Capability-Centric Solution for Framework Architecture……Page 379
12.5.5 Layers and Associated Capabilities……Page 380
Process Layer……Page 381
12.6.1 Proof of Concept Showcase……Page 382
SSDSS Example……Page 383
SSCS Example……Page 384
SSADS Example……Page 385
SSMVS and SSFS Example……Page 386
12.7 Conclusions……Page 387
References……Page 388
13.1 Introduction……Page 392
13.2 The Biological Domain – HSC Biology……Page 393
13.2.2 The Hematopoiesis Control Mechanisms……Page 394
13.3.1 Experimental Limitations……Page 395
13.3.2 What a Model Can Be Useful For……Page 396
13.4 Drawbacks of Existing Models and Why Agents……Page 397
13.5 Overview of Our Agent Modeling Framework……Page 398
13.5.1 Framework Components……Page 399
Simulation Engine……Page 400
Simulation Engine and Environment……Page 401
Cell Agent Behavior……Page 402
13.6 Agentifying Existing Approaches……Page 403
13.6.1 A Cellular Automata Approach to Modeling Stem Cells……Page 404
13.6.3 Re-formulation Using an Agent-Based Approach……Page 406
13.6.4 Operation……Page 408
13.6.5 Roeder-Loeffler Model of Self-Organization……Page 409
13.7 From Agent Model to Simulation……Page 410
13.7.1 MASON……Page 411
13.7.2 Implementation of CELL in MASON……Page 412
13.7.3 Implementation of Roeder-Loeffler Model in MASON……Page 413
13.8 Discussion……Page 415
13.8.1 Concluding Remarks……Page 417
References……Page 418
Part IV: Tools……Page 422
14.1 Introduction……Page 425
14.2 Needs in Disaster and Rescue Management……Page 427
14.3.1 RoboCup Rescue Project……Page 429
14.3.2 Architecture of the Simulation System……Page 430
14.3.3 Progress of the Simulation……Page 432
14.3.4 World Model and Representation……Page 433
14.3.5 Protocol……Page 434
14.4.1 Lessons from Agent Competitions……Page 436
14.4.2 Researches Related to Real Applications……Page 439
14.5.1 Validity of Agent-Based Simulations……Page 440
Experiment 1: Consistency with other methods……Page 441
Experiment 2: Effect of shape of disaster-struck areas……Page 442
14.6.1 Agent Behavior Formulation and Presentation……Page 443
2004 Challenge Session……Page 446
Time Sequence Analysis of Disaster Simulations……Page 447
Architecture of the ABSS……Page 449
Assessment of Simulation Results……Page 450
References……Page 451
15.1 Introduction……Page 453
15.2 Programming Languages for Multi-Agent Systems……Page 454
15.3.1 Language……Page 455
15.3.2 Interpreter……Page 458
15.4.1 Environments……Page 461
15.4.2 Execution Modes……Page 463
15.4.3 Internal Actions……Page 464
15.4.4 Customized Architectures……Page 465
15.5 Example……Page 466
15.5.1 Environment……Page 467
15.5.2 Agents……Page 469
15.6 Ongoing Projects……Page 473
References……Page 475
16.1 Introduction……Page 479
16.2 Simulation Study and User Roles……Page 480
16.2.1 Tasks and User Roles……Page 481
16.2.2 User Involvement in Agent-Based Simulation Tools……Page 482
16.2.3 Tools for Agent-Based Simulation in General……Page 483
16.3 Core SeSAm……Page 484
Primitives, User Functions and Data Types……Page 485
Structural Description……Page 487
Declaration of Situations……Page 489
16.3.2 Simulation Routine and Model Interpretation……Page 490
Agent Update……Page 491
Properties of the Update Scheme……Page 492
16.3.4 Problematic Details of the Language……Page 493
16.3.5 General Aspects of Suitability……Page 494
16.4.1 Principles……Page 495
Forms and Tables……Page 496
Behavior Specification……Page 498
16.4.4 Experiment Scripting and DAVINCI for Experimenters……Page 500
16.4.5 Online Aggregated Data Presentation and Animation……Page 501
16.4.7 Agent Playing for Advanced Participation……Page 503
16.5.1 Novices……Page 505
16.6 General Discussion and Future Work……Page 506
References……Page 508
Modeling and Simulation……Page 510
Simulation Environments……Page 511
17.2 JAMES II……Page 513
17.3.1 A Modeling Formalism for the Description of Multi-Agent Systems……Page 515
17.3.3 Representing Models……Page 522
17.4 Application……Page 525
17.4.1 Composition Structure of the MANET Model……Page 526
17.4.2 Equipping Nodes with Alternative User Models……Page 527
17.5.1 Experiment Definition……Page 529
17.7 Outlook……Page 531
References……Page 532
Glossary……Page 535
Reviews
There are no reviews yet.