Agent-Based Modeling and Simulation with Swarm

Chapman & Hall/CRC Studies in Informatics Series
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Introduction What is simulation? Simulation of intelligence Criticism of simulation Swarm and the Santa Fe Institute Evolutionary Methods and Evolutionary Computation What is evolutionary computation? What are genetic algorithms? What is genetic programming? What is interactive evolutionary computation? Multi-Agent Simulation Based on Swarm Overview of Swarm Tutorial Evolutionary Simulation Simulation of sexual selection Swarm-based simulation of sexual selection Simulation of the prisoner's dilemma Evolving artificial creatures and artificial life Ant Colony-Based Simulation Collective behaviors of ants Swarm simulation of the pheromone trails of ants Ant colony optimization (ACO) Ant-clustering algorithms Swarm-based simulation of ant-clustering Ant colony-based approach to the network routing problem Ant-based job separation Emergent cooperation of army ants Particle Swarm Simulation Boids and flocking behaviors Simulating boids with Swarm Swarm chemistry PSO: particle swarm optimization ABC algorithm BUGS: a bug-based search strategy BUGS in Swarm Cellular Automata Simulation Game of life Conway class with Swarm Program that replicates itself Simulating forest fires with Swarm Segregation model simulation with Swarm Lattice gas automaton Turing model and morphogenesis simulation Simulating percolation with Swarm Silicon traffic and its control The world of Sugarscape Conclusion Appendix A: GUI Systems and Source Code Appendix B: Installing Swarm References Index Swarm Index Name Index
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.

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Autor: Hitoshi (University of Tokyo Iba
ISBN-13 :: 9781466562349
ISBN: 146656234X
Erscheinungsjahr: 17.07.2013
Verlag: Taylor & Francis Inc
Gewicht: 632g
Seiten: 317
Sprache: Englisch
Sonstiges: Buch, 243x156x22 mm, There is an 8-page color insert that follows page 44; 35 Tables, black and white; 11 Illustrations, color; 192 Illustrations, black and white
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