Emergence by strategy: Flocking boids and their fitness in relation to model complexity

Michael Wagner, Wentong Cai, M. Lees
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引用次数: 10

Abstract

In this paper we aim to examine emergent properties of agent-based models by using evolutionary algorithms. Taking the model of flocking boids as an example, we study and try to understand how the pressure of natural selection towards strategic behavior can result in emergent behavior. Furthermore we investigate how an increase of complexity in the model affects those properties and discover some counter-intuitive behavior in the process.
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策略涌现:群聚体及其与模型复杂性的适应度关系
在本文中,我们的目的是通过使用进化算法来研究基于智能体的模型的涌现特性。以群集模型为例,我们研究并试图理解自然选择对策略行为的压力如何导致紧急行为。此外,我们研究了模型复杂性的增加如何影响这些特性,并发现了过程中的一些反直觉行为。
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Stochastic root finding for optimized certainty equivalents Uncertainty modeling and simulation of tool wear in mechanized tunneling The knowledge gradient algorithm using locally parametric approximations Emergence by strategy: Flocking boids and their fitness in relation to model complexity Time management in hierarchical federation using RTI-RTI interoperation
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