Preface |
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vii | |
Acknowledgments |
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ix | |
About the Editors |
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xi | |
Contributors |
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xiii | |
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Multi-Agent Systems and Simulation: A Survey from the Agent Community's Perspective |
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3 | (50) |
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3 | (2) |
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M&S for MAS: The DAI Case |
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5 | (2) |
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MACE: Toward Modern Generic MAS Platform |
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MAS for M&S: Building Artificial Laboratories |
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7 | (6) |
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The Need for Individual-Based Modeling |
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The Microsimulation Approach: The Individual-Based Modeling Forerunner |
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The Agent-Based Modeling Approach |
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Agent-Based Social Simulation: Simulating Human-Inspired Behaviors |
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Flocks and Ants: Simulating Artificial Animats |
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Simulating MAS: Basic Principles |
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13 | (16) |
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Simulating MAS as Three Correlated Modeling Activities |
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A Still-Incomplete Picture |
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The Zeigler's Framework for Modeling and Simulation |
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29 | (3) |
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Modeling Relation: Validity |
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Simmulation Relation; Simulator Correctness |
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Deriving Three Fundamental Questions |
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Studying MAS Simulatons Using the Framework for M&S |
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32 | (9) |
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Does the Model Accurately Represent the Source System? |
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Does the Model Accommodate the Experimetnal Frame? |
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Is the simulator Correct? |
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41 | (1) |
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42 | (11) |
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Multi-Agent Systems and Simulation: A Survey from an Application Perspective |
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53 | (24) |
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Simulation in the Science of Complex Systems |
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53 | (3) |
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Predecessors and Alternatives |
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56 | (8) |
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Simulation of Voting Behavior in the Early Sixties |
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Dynamic Microsimulation to Predict Demographic Processes |
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Cellular Automata: Simple Agents Moving on a Topography |
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Discrete Event Simulation |
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Similarities and Differences Among these Approaches |
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Unfolding, Nesting, Coping with Complexity |
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64 | (5) |
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Agents with Different Roles in Different Environments |
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The Role of the Environment |
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Issues for Future Research: The Emergence of Communication |
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69 | (2) |
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71 | (6) |
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Simulation Engines for Multi-Agent Systems |
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77 | (32) |
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Georgios K. Theodorlopoulos |
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77 | (1) |
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Multi-Agent System Architectures |
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78 | (1) |
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Discrete Event System Simulation Engines for MAS |
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79 | (7) |
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The Discrete Event Simulation Paradigm |
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A Survey of MAS Simulation Toolkits |
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Taxonmoy of Discrete Event Simulation Toolkits |
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Parallel Simulation Enginges for MAS |
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86 | (10) |
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Parallel discrete Event Simulation |
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The High Level Architecture and Simulation Interoperability |
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A Survey of Parallel MAS Simulation Toolkits |
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Taxonomy of Parallel DES Toolkits for MAS |
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Issues for Future Research |
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96 | (3) |
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99 | (10) |
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Part II: Simulation for MAS |
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Polyagents: Simulation for Supporting Agents' Decision Making |
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109 | (24) |
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109 | (2) |
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111 | (7) |
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A Challenge for simulation-Based Decision Making |
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Some Applicatons of Polyagents |
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118 | (6) |
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Factory Scheduling and Control |
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124 | (3) |
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Theoretical Opportunities |
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127 | (1) |
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128 | (5) |
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Combining Simulation and Formal Tools for Developing Self-Organizing MAS |
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133 | (34) |
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133 | (3) |
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The A&A Meta-Model for Self-Organizing Systems |
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136 | (3) |
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The Role of Environment in Self-Organizing Systems |
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Overview of the A&A Meta-Model |
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Methodolocical Issues Raised by Self-Organizing Systems |
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139 | (1) |
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A Methodological Approach for Engineering Self-Organizing MAS |
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140 | (4) |
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Using the PRISM Tool to Support the Method |
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144 | (1) |
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Case Study: Plain Diffusion |
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145 | (13) |
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Simulating Plain Diffusion |
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Verifying Plain diffusion |
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Preliminary Scalability Analysis |
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158 | (3) |
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161 | (6) |
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On the Role of Software Architecture for Simulating Multi-Agent Systems |
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167 | (48) |
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167 | (2) |
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169 | (3) |
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A System and Its Environment |
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Characteristics of Multi-Agent Control Systems |
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Software-in-the-Loop Simulation Mode |
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AGV Transportation System |
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172 | (5) |
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Physical Setup of an AGV Transportation System |
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Requirements of an AGV Simulator |
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Modeling Multi-Agent Control Applications in Dynamic Environments |
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177 | (8) |
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Overview of Modeling Framework |
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Simulation Model of the AGV Transportation System |
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Architecture of the Simulation Platform |
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185 | (16) |
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Top-Level Module Decomposition View of the simulation |
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Component and Connector View of the Simulated Environment |
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Component and Connector View of the Simulation Engine |
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An Aspect-Oriented Approach to Embed Control Software |
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Component and Connector View of the Execution Tracker |
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Evaluating the AGV Simulator |
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201 | (4) |
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Flexibility of the AGV Simulator |
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Measurements of the AGV Simulator |
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Development Supported by the AGV Simulator |
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205 | (4) |
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Special-Purpose Simulation Platforms |
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Embedding the Control software |
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Conclusions and future Work |
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209 | (2) |
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Concrete Directions for future research |
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211 | (4) |
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Replicator Dynamics in Discrete and Continuous Strategy Spaces |
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215 | (28) |
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215 | (2) |
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Elementary Concepts from Game Theory |
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217 | (7) |
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Strategic Games with discrete Strategy Sets |
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Strategic Games with Continuous Strategy Spaces |
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224 | (4) |
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Repliator Dynamics in Discrete Strategy Spaces |
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Peplicator Dynamics in Continuous Stratey Spaces |
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Evolutionary dynamics in discrete Strategy Spaces |
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228 | (4) |
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Anallysis of the Evolutionary dynamics of the Categorization of Games |
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Relatin Evolutionary Dymamics in discrete Strategy Spaces with Reinforcement Learning |
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Evolutionary Dynamics in Continuous Strategy spaces |
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232 | (5) |
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Mutation as Engine for diffusion in Continuous Strategy Spaces |
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Non-Isotropic Mutations and Evolutionary Flows in Strategy Spaces |
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Draining of Pay-Off Streams in Strategy Space |
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Example of the resulting dynamics of the Continuous Replicator Equations |
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237 | (3) |
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240 | (1) |
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240 | (3) |
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Stigmergic Cues and Their Uses in Coordination: An Evolutionary Approach |
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243 | (28) |
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243 | (2) |
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Stigmergy: Widening the Notion but Not Too Much |
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245 | (3) |
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Stigmergic Cues as Practical Behavioral Traces |
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Two Uses of Stigmergy in Coordintion |
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248 | (3) |
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Coordinatin and cues of Interference |
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Communication and Implicit Signals |
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Stigmergic Self-Adjustment and Stigmergic Communication |
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Stigmergy in Cooperation and Competition |
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251 | (2) |
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Cooperative and Competitive interference |
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Collaborative and Conflictual Stigmergic Coordination |
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Why Pheromones Are Not Stigmergic Cues |
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The Role of Stigmergy in the Evolution of Pheromonal Communication |
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Understanding Stigmergy through Evolution |
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253 | (7) |
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Evolution of Practical Behavior |
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Evolutin of Stigmergic Self-Adjustment and Indirect Coordination |
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Evolution of Stigmergic Communication |
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260 | (1) |
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261 | (1) |
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262 | (1) |
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262 | (9) |
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Part III: MAS for Simulation |
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Challenges of Country Modeling with Databases, Newsfeeds, and Expert Surveys |
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271 | (30) |
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271 | (3) |
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274 | (4) |
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Major PMF Models within Each PMFserve Subsystem |
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Social Agents, Factions, and the FactionsSim Testbed |
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278 | (4) |
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Overview of Some Existing Coutry Database |
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282 | (2) |
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Overview of Automated Data Extraction Technology |
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284 | (5) |
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Overview of Subject Matter Expert Studies/surveys |
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289 | (1) |
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Overview of Integrative Knowledge Engineering Process |
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290 | (6) |
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296 | (1) |
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297 | (1) |
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298 | (3) |
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Crowd Behavior Modeling: From Cellular Automata to Multi-Agent Systems |
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301 | (24) |
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301 | (2) |
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Pedestrian Dynamics Context: An Overview |
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303 | (3) |
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Pedestrians and Particles |
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Pedestrians as States of CA |
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Pedestrians as Autonomous Agents |
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Guidelines for Crowds Modeling with Situated Cellula Agents Approach |
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306 | (4) |
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Spatial Infrastructure and Active Elements of the environmentf |
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A Pedestrian Modeling Scenario |
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310 | (5) |
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From a SCA Model to Its Implemintaion |
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315 | (4) |
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Supporting and Executing SCA Models |
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319 | (2) |
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Discussin and Future Reasearch Directions |
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321 | (1) |
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321 | (1) |
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321 | (4) |
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Agents for Traffic simulation |
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325 | (32) |
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325 | (2) |
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Agents for Traffic Simulation |
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327 | (4) |
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Macroscopic vs. Microscopic Approaches |
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Models for the Driving Task |
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331 | (7) |
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The Intelligent Driver Model |
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Modeling Discrete Decisions |
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338 | (3) |
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Approaching a Traffic Light |
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Microscopic Traffic Simulation Software |
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341 | (5) |
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From Individual to Collective Properties |
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346 | (6) |
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Emergence of Stop-and-Go Waves |
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Store-and-Forward Strategy for Inter-Vehicle Communication |
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Conclusionms and future Work |
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352 | (1) |
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352 | (5) |
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An Agent-Based Generic Framework for Symbiotic simulation Systems |
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357 | (32) |
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357 | (2) |
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Concepts of Symbiotic Simulation |
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359 | (2) |
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Different Classes of Symbiotic Simulation Systems |
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361 | (4) |
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Symbiotic Simulation Decision Support Systems (SSDSS) |
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Symbiotic Simulation Control Systems (SSCS) |
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Symbiotic Simulation Forecasting Systems (SSFS) |
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Symbiotic Simulaton odel Validation Systems (SSMVS) |
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Symbiotic Simulation Anomaly Detection Systems (SSADS) |
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Hybrid Symbiotic Simulation Systems |
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Symbiotic Simulation Anomaly Detection Systems |
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Symbiotic Simulation Systems at a Glance |
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Workflows and Activities in Symbiotic Simulation Systems |
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365 | (7) |
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372 | (6) |
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Discussion of Existing Architectures |
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Web Service approach vs. Agent-Based Approach |
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Capability-Centric Solution for Framework Architecture |
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Layers and Associated Capabilities |
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378 | (5) |
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Proof of Concept Showcase |
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383 | (1) |
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384 | (1) |
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384 | (5) |
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Agent -Based Modeling of Stem Cells |
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389 | (34) |
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389 | (1) |
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The Biological Domain-HSC Biology |
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390 | (2) |
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392 | (2) |
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What a Model Can Be Useful For |
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Drawbacks of Existing Models and Why Agents |
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394 | (1) |
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Overview of Our Agent Modeling Frmework |
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395 | (5) |
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Interfaces Between th Framework Components |
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Behavior of the Framework Components |
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Extending Our Agent Modeling Framework |
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Agentifying Existing Approaches |
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400 | (7) |
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A Cellula Automata Approach to Modeling Stem Cells |
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Discussion about the Cellular Automata Approach |
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Re-formulation Using an Agent-Based Approach |
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Roeder-Loeffler Model of Self-Organization |
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From Agent Model to Simulation |
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407 | (5) |
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Implementation of CELL in MASON |
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Implementation of Roder-Loeffler Model in MASON |
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412 | (3) |
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415 | (1) |
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415 | (8) |
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RoboCup Rescue: Challenges and Lesssons Learned |
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423 | (28) |
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423 | (2) |
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Needs in Disaster and Rescue Managemetn |
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425 | (2) |
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Architecture of RoboCup Rescue Simulation System and Usage as MAS Platform |
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427 | (7) |
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Architecture of the Simulation System |
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Progress of the Simulation |
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World Model and representaion |
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Lessons Learned from RoboCup Rescue Competitions |
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434 | (4) |
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Lessons Learned from Agent Competitions |
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Researches Related to Real Applications |
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Examples and Discussions on Experiments Using Real Maps |
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438 | (3) |
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Validity of Agent-Based Simulations |
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Discussion on Experiments |
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Analysis of ABSS Based on Probability Model |
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441 | (6) |
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Agent Behavior Formulation and Presentation |
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Analysis results of RoboCup rescue Competition Logs |
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447 | (2) |
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449 | (2) |
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Agent-Based Simulation Using BDI Programming in Jason |
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451 | (26) |
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451 | (1) |
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Programming Languaes for Multi-Agent Systems |
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452 | (1) |
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Programming Multi-Agent Systems Using Jason |
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453 | (6) |
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Jason Features for simulation |
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459 | (5) |
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464 | (7) |
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471 | (2) |
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473 | (1) |
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473 | (4) |
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SeSAm: Visual Programming and Participatory Simulation for Agent-Based Models |
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477 | (32) |
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477 | (1) |
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Simulation Study and User Roles |
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478 | (4) |
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User iNvolvemetn in Agent-Based Simulation Tools |
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Tools for Agent-Based Simulation in General |
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482 | (11) |
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Basic Model Representation |
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Simulation Routine and Model Interpretation |
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Plugin Mechanism for Extension |
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Problematic Details of the Language |
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General Aspects of Suitablity |
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493 | (10) |
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Developing a Conceptual Model |
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Visual Programming for Model Implementation |
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Experimetn Scripting and DAVINCI for Experimenters |
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Online Aggregated Data Presentation and Animation |
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Model-Specific interfaces |
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Agent Playing for Advanced participation |
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503 | (1) |
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Knowledgeable in Implementation, Not in Multi-Agent systems |
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General Discussion and Future Work |
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504 | (2) |
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506 | (1) |
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506 | (3) |
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JAMES II- Experiences and Interpretations |
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509 | (26) |
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509 | (3) |
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512 | (2) |
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Using JAMES II for Multi-Agent Modeling and Simulation |
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Multi-Agent Modeling and Simulation in JAMES II |
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514 | (10) |
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A Modeling Formalism for the Description of Multi-Agent Systems |
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524 | (4) |
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Composition Structure of the MANET Model |
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Equpping Nodes with Alternative User Models |
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528 | (2) |
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Experiences and Interpretations |
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530 | (1) |
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530 | (1) |
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531 | (1) |
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531 | (4) |
Glossary |
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535 | (8) |
Index |
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543 | |