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E-raamat: Multi-Agent Systems: Simulation and Applications [Taylor & Francis e-raamat]

Edited by (University of Rostock, GERMANY), Edited by (Katholieke Universiteit Leuven, Belgium)
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Methodological Guidelines for Modeling and Developing MAS-Based Simulations

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 fields 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.
Preface vii
Acknowledgments ix
About the Editors xi
Contributors xiii
Part I: Background
Multi-Agent Systems and Simulation: A Survey from the Agent Community's Perspective
3(50)
Fabin Michel
Jacques Ferber
Alexis Drogoul
Introduction
3(2)
M&S for MAS: The DAI Case
5(2)
The CNET Simulator
The DVMT Project
MACE: Toward Modern Generic MAS Platform
MAS for M&S: Building Artificial Laboratories
7(6)
The Need for Individual-Based Modeling
The Microsimulation Approach: The Individual-Based Modeling Forerunner
The Agent-Based Modeling Approach
Agent-Based Social Simulation: Simulating Human-Inspired Behaviors
Flocks and Ants: Simulating Artificial Animats
Simulating MAS: Basic Principles
13(16)
Agent
Environment
Interactions
Modeling Time
Simulating MAS as Three Correlated Modeling Activities
A Still-Incomplete Picture
The Zeigler's Framework for Modeling and Simulation
29(3)
Source System
Experimental Frame
Model
Simulator
Modeling Relation: Validity
Simmulation Relation; Simulator Correctness
Deriving Three Fundamental Questions
Studying MAS Simulatons Using the Framework for M&S
32(9)
Does the Model Accurately Represent the Source System?
Does the Model Accommodate the Experimetnal Frame?
Is the simulator Correct?
Conclusion
41(1)
References
42(11)
Multi-Agent Systems and Simulation: A Survey from an Application Perspective
53(24)
Klaus G. Troitzsch
Simulation in the Science of Complex Systems
53(3)
Predecessors and Alternatives
56(8)
Simulation of Voting Behavior in the Early Sixties
Dynamic Microsimulation to Predict Demographic Processes
Cellular Automata: Simple Agents Moving on a Topography
Discrete Event Simulation
Similarities and Differences Among these Approaches
Unfolding, Nesting, Coping with Complexity
64(5)
Agents with Different Roles in Different Environments
The Role of Interactions
The Role of the Environment
Issues for Future Research: The Emergence of Communication
69(2)
Agent Communication
Concluding Remarks
References
71(6)
Simulation Engines for Multi-Agent Systems
77(32)
Georgios K. Theodorlopoulos
Rob Minson
Roland Ewald
Michael Lees
Introduction
77(1)
Multi-Agent System Architectures
78(1)
Discrete Event System Simulation Engines for MAS
79(7)
The Discrete Event Simulation Paradigm
A Survey of MAS Simulation Toolkits
Taxonmoy of Discrete Event Simulation Toolkits
Parallel Simulation Enginges for MAS
86(10)
Parallel discrete Event Simulation
The High Level Architecture and Simulation Interoperability
A Survey of Parallel MAS Simulation Toolkits
Taxonomy of Parallel DES Toolkits for MAS
Issues for Future Research
96(3)
References
99(10)
Part II: Simulation for MAS
Polyagents: Simulation for Supporting Agents' Decision Making
109(24)
H. Van Dyke Parunak
Sven A. Brueckner
Introduction
109(2)
The Polyagent Model
111(7)
A Challenge for simulation-Based Decision Making
Two Big Ideas
The Architecture
The Environment
Related Work
Some Applicatons of Polyagents
118(6)
Factory Scheduling and Control
Vehicle Routing
Prediction
Future Research
124(3)
Theoretical Opportunities
New Applications
Conclusion
127(1)
References
128(5)
Combining Simulation and Formal Tools for Developing Self-Organizing MAS
133(34)
Luca Gardelli
Mirko Viroli
Andrea Omicini
Introduction
133(3)
The A&A Meta-Model for Self-Organizing Systems
136(3)
The Role of Environment in Self-Organizing Systems
Overview of the A&A Meta-Model
An Architectural Pattern
Methodolocical Issues Raised by Self-Organizing Systems
139(1)
A Methodological Approach for Engineering Self-Organizing MAS
140(4)
Overview
Modeling
Simultion
Verfication
Tuning
Using the PRISM Tool to Support the Method
144(1)
Modeling
Simulation
Verification
Tuning
Case Study: Plain Diffusion
145(13)
Problem Statement
Modeling Plain Diffusion
Simulating Plain Diffusion
Verifying Plain diffusion
Tunning Plain diffusion
Preliminary Scalability Analysis
Conclusion
158(3)
References
161(6)
On the Role of Software Architecture for Simulating Multi-Agent Systems
167(48)
Alexeander Helleboogh
Danny Weyns
Tom Holvoet
Introduction
167(2)
Background
169(3)
A System and Its Environment
Characteristics of Multi-Agent Control Systems
Software-in-the-Loop Simulation Mode
AGV Transportation System
172(5)
Physical Setup of an AGV Transportation System
AGV Control System
Requirements of an AGV Simulator
Modeling Multi-Agent Control Applications in Dynamic Environments
177(8)
Overview of Modeling Framework
Simulation Model of the AGV Transportation System
Architecture of the Simulation Platform
185(16)
Requirements
Top-Level Module Decomposition View of the simulation
Platform
Component and Connector View of the Simulated Environment
Component and Connector View of the Simulation Engine
An Aspect-Oriented Approach to Embed Control Software
Component and Connector View of the Execution Tracker
Evaluating the AGV Simulator
201(4)
Flexibility of the AGV Simulator
Measurements of the AGV Simulator
Multi-Agent System
Development Supported by the AGV Simulator
Related Work
205(4)
Special-Purpose Simulation Platforms
Embedding the Control software
Conclusions and future Work
209(2)
Concrete Directions for future research
Closing Reflection
References
211(4)
Replicator Dynamics in Discrete and Continuous Strategy Spaces
215(28)
Karl Tuyls
Ronald Westra
Introduction
215(2)
Elementary Concepts from Game Theory
217(7)
Strategic Games with discrete Strategy Sets
Strategic Games with Continuous Strategy Spaces
Replicator Dynamics
224(4)
Repliator Dynamics in Discrete Strategy Spaces
Peplicator Dynamics in Continuous Stratey Spaces
Evolutionary dynamics in discrete Strategy Spaces
228(4)
Anallysis of the Evolutionary dynamics of the Categorization of Games
Relatin Evolutionary Dymamics in discrete Strategy Spaces with Reinforcement Learning
Evolutionary Dynamics in Continuous Strategy spaces
232(5)
Mutation as Engine for diffusion in Continuous Strategy Spaces
Non-Isotropic Mutations and Evolutionary Flows in Strategy Spaces
Draining of Pay-Off Streams in Strategy Space
Example of the resulting dynamics of the Continuous Replicator Equations
237(3)
Conclusions
240(1)
References
240(3)
Stigmergic Cues and Their Uses in Coordination: An Evolutionary Approach
243(28)
Luca Tummolini
Marco Mirolli
Cristiano Castelfranchi
Introduction
243(2)
Overview
Stigmergy: Widening the Notion but Not Too Much
245(3)
A Tale of a Wrong Story
Is It Communication?
Stigmergic Cues as Practical Behavioral Traces
Two Uses of Stigmergy in Coordintion
248(3)
Coordinatin and cues of Interference
Communication and Implicit Signals
Stigmergic Self-Adjustment and Stigmergic Communication
Stigmergy in Cooperation and Competition
251(2)
Cooperative and Competitive interference
Collaborative and Conflictual Stigmergic Coordination
Why Pheromones Are Not Stigmergic Cues
The Role of Stigmergy in the Evolution of Pheromonal Communication
Understanding Stigmergy through Evolution
253(7)
The Basic Model
Evolution of Practical Behavior
Evolutin of Stigmergic Self-Adjustment and Indirect Coordination
Evolution of Stigmergic Communication
Future Work
260(1)
Conclusion
261(1)
Acknowledgments
262(1)
References
262(9)
Part III: MAS for Simulation
Challenges of Country Modeling with Databases, Newsfeeds, and Expert Surveys
271(30)
Barry G. Silverman
Gnana K. Bharthy
G. Jiyun Kim
Introduction
271(3)
Cognitive Agent Modeling
274(4)
Major PMF Models within Each PMFserve Subsystem
Social Agents, Factions, and the FactionsSim Testbed
278(4)
Overview of Some Existing Coutry Database
282(2)
Overview of Automated Data Extraction Technology
284(5)
Overview of Subject Matter Expert Studies/surveys
289(1)
Overview of Integrative Knowledge Engineering Process
290(6)
Concluding Remarks
296(1)
Acknowledgment
297(1)
References
298(3)
Crowd Behavior Modeling: From Cellular Automata to Multi-Agent Systems
301(24)
Stefania Bandini
Sara Manzoni
Giuseppe Vizzari
Introduction
301(2)
Pedestrian Dynamics Context: An Overview
303(3)
Pedestrians and Particles
Pedestrians as States of CA
Pedestrians as Autonomous Agents
Guidelines for Crowds Modeling with Situated Cellula Agents Approach
306(4)
Spatial Infrastructure and Active Elements of the environmentf
Pedestrians
A Pedestrian Modeling Scenario
310(5)
The Scenario
The Modeling Assumptions
The Environment
The Passengers
Simulation Results
From a SCA Model to Its Implemintaion
315(4)
Supporting and Executing SCA Models
Conclusions
319(2)
Discussin and Future Reasearch Directions
321(1)
Acknowledgemets
321(1)
References
321(4)
Agents for Traffic simulation
325(32)
Arne Kesting
Martin Treiber
Dirk Helbing
Introduction
325(2)
Aim and Overview
Agents for Traffic Simulation
327(4)
Macroscopic vs. Microscopic Approaches
Driver-Vehicle Agents
Models for the Driving Task
331(7)
The Intelligent Driver Model
Inter-Driver Variability
Intra-Drever Variability
Modeling Discrete Decisions
338(3)
Modeling Lane Changes
Approaching a Traffic Light
Microscopic Traffic Simulation Software
341(5)
Simmlator Design
Numerical Integration
Visualization
From Individual to Collective Properties
346(6)
Emergence of Stop-and-Go Waves
Impact of a Speed Limit
Store-and-Forward Strategy for Inter-Vehicle Communication
Conclusionms and future Work
352(1)
References
352(5)
An Agent-Based Generic Framework for Symbiotic simulation Systems
357(32)
Heiko Aydt
Stephen John Turner
Wentong Cai
Malcolm Yoke Hean Low
Introduction
357(2)
Concepts of Symbiotic Simulation
359(2)
Different Classes of Symbiotic Simulation Systems
361(4)
Symbiotic Simulation Decision Support Systems (SSDSS)
Symbiotic Simulation Control Systems (SSCS)
Symbiotic Simulation Forecasting Systems (SSFS)
Symbiotic Simulaton odel Validation Systems (SSMVS)
Symbiotic Simulation Anomaly Detection Systems (SSADS)
Hybrid Symbiotic Simulation Systems
Symbiotic Simulation Anomaly Detection Systems
Symbiotic Simulation Systems at a Glance
Workflows and Activities in Symbiotic Simulation Systems
365(7)
Workflows
Activities
Agent-Based Framework
372(6)
Architecture Requiremens
Discussion of Existing Architectures
Web Service approach vs. Agent-Based Approach
Capability-Centric Solution for Framework Architecture
Layers and Associated Capabilities
Applications
378(5)
Proof of Concept Showcase
Conceptual Showcases
Conclusions
383(1)
Future Work
384(1)
References
384(5)
Agent -Based Modeling of Stem Cells
389(34)
Mark D'Inverno
Paul Howells
Sara Montagna
Ingo Roeder
Rob Saunders
Introduction
389(1)
Overview
The Biological Domain-HSC Biology
390(2)
The Hematopoietic System
The Hematopoiesis
Control Mechanisms
Stem Cell Modeling
392(2)
Experimenal Limitations
What a Model Can Be Useful For
Drawbacks of Existing Models and Why Agents
394(1)
Overview of Our Agent Modeling Frmework
395(5)
Framework Components
Interfaces Between th Framework Components
Behavior of the Framework Components
Extending Our Agent Modeling Framework
Agentifying Existing Approaches
400(7)
A Cellula Automata Approach to Modeling Stem Cells
Discussion about the Cellular Automata Approach
Re-formulation Using an Agent-Based Approach
Opetation
Roeder-Loeffler Model of Self-Organization
From Agent Model to Simulation
407(5)
MASON
Implementation of CELL in MASON
Implementation of Roder-Loeffler Model in MASON
Discussion
412(3)
Acknowledgments
415(1)
References
415(8)
Part IV: Tools
RoboCup Rescue: Challenges and Lesssons Learned
423(28)
Tomoichi Takahashi
Introductin
423(2)
Needs in Disaster and Rescue Managemetn
425(2)
Architecture of RoboCup Rescue Simulation System and Usage as MAS Platform
427(7)
RoboCup rescue Project
Architecture of the Simulation System
Progress of the Simulation
World Model and representaion
Protocol
Lessons Learned from RoboCup Rescue Competitions
434(4)
Lessons Learned from Agent Competitions
Researches Related to Real Applications
Examples and Discussions on Experiments Using Real Maps
438(3)
Validity of Agent-Based Simulations
Discussion on Experiments
Analysis of ABSS Based on Probability Model
441(6)
Agent Behavior Formulation and Presentation
Analysis results of RoboCup rescue Competition Logs
Discussion and Summary
447(2)
References
449(2)
Agent-Based Simulation Using BDI Programming in Jason
451(26)
Rafael H. Bordini
Jomi F. Hubner
Introduction
451(1)
Programming Languaes for Multi-Agent Systems
452(1)
Programming Multi-Agent Systems Using Jason
453(6)
Language
Interpreter
Jason Features for simulation
459(5)
Environments
Execution Modes
Internal Actions
Customized Architectures
Example
464(7)
Environment
Agents
Results
Ongoung Projects
471(2)
Conclusion
473(1)
References
473(4)
SeSAm: Visual Programming and Participatory Simulation for Agent-Based Models
477(32)
Franziska Klugl
Introduction
477(1)
Simulation Study and User Roles
478(4)
Tasks and User Roles
User iNvolvemetn in Agent-Based Simulation Tools
Tools for Agent-Based Simulation in General
Core SeSAm
482(11)
Basic Model Representation
Simulation Routine and Model Interpretation
Plugin Mechanism for Extension
Problematic Details of the Language
General Aspects of Suitablity
Users, Tasks and SeSAm
493(10)
Principles
Developing a Conceptual Model
Visual Programming for Model Implementation
Experimetn Scripting and DAVINCI for Experimenters
Online Aggregated Data Presentation and Animation
Model-Specific interfaces
Agent Playing for Advanced participation
Experiences
503(1)
Novices
Knowledgeable in Implementation, Not in Multi-Agent systems
General Discussion and Future Work
504(2)
Acknowledgment
506(1)
References
506(3)
JAMES II- Experiences and Interpretations
509(26)
Jan Himmelspach
Mathias Rohl
Introduction
509(3)
JAMES II
512(2)
Using JAMES II for Multi-Agent Modeling and Simulation
Multi-Agent Modeling and Simulation in JAMES II
514(10)
A Modeling Formalism for the Description of Multi-Agent Systems
Simulation Algorithms
Representing Models
Application
524(4)
Composition Structure of the MANET Model
Equpping Nodes with Alternative User Models
The Role of Time
528(2)
Experiment Definition
Experiences and Interpretations
530(1)
Outlook
530(1)
Acknowledgment
531(1)
References
531(4)
Glossary 535(8)
Index 543
Adelinde M. Uhrmacher, Danny Weyns