Agent based modeling and simulation with Swarm 1st Edition by Hitoshi Iba 146656234X 9781466562349
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ISBN 10: 146656234X
ISBN 13: 9781466562349
Author: Hitoshi Iba
Swarm-based multi-agent simulation leads to better modeling of tasks in biology, engineering, economics, art, and many other areas. It also facilitates an understanding of complicated phenomena that cannot be solved analytically. Agent-Based Modeling and Simulation with Swarm provides the methodology for a multi-agent-based modeling approach that integrates computational techniques such as artificial life, cellular automata, and bio-inspired optimization. Each chapter gives an overview of the problem, explores state-of-the-art technology in the field, and discusses multi-agent frameworks. The author describes step by step how to assemble algorithms for generating a simulation model, program, method for visualization, and further research tasks. While the book employs the commonly used Swarm system, readers can model and develop the simulations with their own simulator. To encourage hands-on exploration of emergent systems, Swarm-based software and source codes are available for download from the author’s website. A thorough overview of multi-agent simulation and supporting tools, this book shows how this type of simulation is used to acquire an understanding of complex systems and artificial life. It carefully explains how to construct a simulation program for various applications.
Table of contents:
1 Introduction
1.1 What is simulation?
1.2 Simulation of intelligence
1.3 Criticism of simulation
1.4 Swarm and the Santa Fe Institute
2 Evolutionary Methods and Evolutionary Computation
2.1 What is evolutionary computation?
2.2 What are genetic algorithms?
2.2.1 Data representation
2.2.2 Selection
2.2.3 Genetic operations
2.2.4 Flow of the algorithm
2.2.5 Initialization
2.2.6 Extension of the GA
2.2.7 Traveling salesman problem (TSP)
2.3 What is genetic programming?
2.3.1 Description of individuals in GP
2.3.2 Flow chart of GP
2.3.3 Initialization of tree structures
2.3.4 Fitness evaluation
2.3.5 Crossover and mutation
2.3.6 Simulating wall following robots
2.4 What is interactive evolutionary computation?
2.4.1 Interactive music composition based on Swarm simulation
3 Multi-Agent Simulation Based on Swarm
3.1 Overview of Swarm
3.2 Tutorial
3.2.1 simpleCBug
3.2.2 simpleObjCBug and simpleObjCBug2
3.2.3 simpleSwarmBug
3.2.3.1 Initialization
3.2.3.2 Construction of Agents
3.2.3.3 Construction of Schedules
3.2.3.4 Activation of Swarm
3.2.3.5 State of Execution
3.2.4 simpleSwarmBug2
3.2.5 simpleSwarmBug3
3.2.6 simpleObserverBug
3.2.6.1 Initialization
3.2.6.2 Generation of object
3.2.6.3 Construction of scheduling
3.2.6.4 Activation of Swarm
3.2.7 simpleObserverBug2
3.2.8 simpleExperBug
4 Evolutionary Simulation
4.1 Simulation of sexual selection
4.1.1 Sexual selection in relation to markers, handicaps, and parasites
4.1.2 The Kirkpatrick model
4.1.3 Simulation using GAs
4.2 Swarm-based simulation of sexual selection
4.3 Simulation of the prisoner’s dilemma
4.3.1 The prisoner’s dilemma
4.3.2 Iterated prisoner’s dilemma
4.3.3 IPD using GAs
4.3.4 IPD simulation by Swarm
4.3.5 IPD as spatial games
4.4 Evolving artificial creatures and artificial life
4.4.1 What is artificial life?
4.4.2 Artificial life of Karl Sims
4.4.3 Evolutionary morphology for real modular robots
5 Ant Colony–Based Simulation
5.1 Collective behaviors of ants
5.2 Swarm simulation of the pheromone trails of ants
5.3 Ant colony optimization (ACO)
5.4 Ant-clustering algorithms
5.5 Swarm-based simulation of ant-clustering
5.6 Ant colony–based approach to the network routing problem
5.7 Ant-based job separation
5.8 Emergent cooperation of army ants
5.8.1 Altruism of army ants
5.8.2 Defining the problem
5.8.3 Judgment criteria for entering the altruism state
5.8.3.1 Hypotheses
5.8.3.2 Experiment to verify the hypotheses
5.8.4 Judgment criteria with reference to chain formation
5.8.4.1 What is chain formation?
5.8.4.2 Experiment to verify the chain formation system
5.8.5 Changes in strategy based on number of agents
5.8.5.1 Deciding group behavior of army ants
5.8.6 Comparative experiment
5.8.7 Simulation with fixed role assigned
6 Particle Swarm Simulation
6.1 Boids and flocking behaviors
6.2 Simulating boids with Swarm
6.3 Swarm Chemistry
6.4 PSO: particle swarm optimization
6.4.1 PSO algorithm
6.4.2 Comparison with GA
6.4.3 Examples of PSO applications
6.5 ABC algorithm
6.6 BUGS: a bug-based search strategy
6.6.1 Evolution of predatory behaviors using genetic search
6.6.1.1 Bugs hunt bacteria
6.6.1.2 Effectiveness of sexual reproduction
6.6.2 A bug-based GA search
6.7 BUGS in Swarm
7 Cellular Automata Simulation
7.1 Game of life
7.1.1 Rule 184
7.2 Conway class with Swarm
7.3 Program that replicates itself
7.4 Simulating forest fires with Swarm
7.4.1 Simulation of forest fires
7.5 Segregation model simulation with Swarm
7.5.1 Swarm-based simulation of segregation model
7.6 Lattice gas automaton
7.6.1 LGA simulation with Swarm
7.7 Turing model and morphogenesis simulation
7.7.1 Simulation of morphogenesis by the Turing model
7.8 Simulating percolation with Swarm
7.9 Silicon traffic and its control
7.9.1 Simulating traffic jams with Swarm
7.10 The world of Sugarscape
7.10.1 A simple Sugarscape model
7.10.2 Life and birth
7.10.3 Breeding
7.10.4 Environmental changes
7.10.4.1 Nutritive ratio
7.10.4.2 Alternating seasons
7.10.4.3 Generation of pollution
7.10.5 Introduction of culture
7.10.6 Introduction of combat
7.10.7 Introduction of trade
7.10.8 Swarm simulation in Sugarscape
8 Conclusion
Appendix A GUI Systems and Source Code
A.1 Introduction
A.2 PSO simulator and benchmark functions
A.3 TSP simulator by a GA
A.4 Wall-following simulator by GP
A.5 CG synthesis of plants based on L-systems
A.6 LGPC for art
Appendix B Installing Swarm
B.1 Installation
B.1.1 Java2 SDK installation
B.1.2 Installing the Swarm package
B.1.3 Setting environment variables
B.1.4 Compiling
B.1.5 Confirming operation
B.2 Objective-C version
B.2.1 Objective-C and Swarm
B.2.2 Material related to the Objective-C version of Swarm
B.2.3 Running Swarm under various environments
B.2.3.1 Windows
B.2.3.2 Unix
B.2.3.3 Mac OS X
B.3 Useful online resources
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Tags: Hitoshi Iba, Agent, modeling, simulation