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Guided self-organizing particle systems for basic problem solving .

机译:指导自组织粒子系统的基本问题解决。

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摘要

In recent years researchers have shown increasing interest in swarm intelligence as a promising approach to adaptive distributed problem solving. Swarm intelligence consists of techniques inspired by nature, especially social insects and aggregations of animals, and even human interactions. They are based on self-organization (a system's overall behavior emerges from the local interactions among its relatively simple components) and are often decentralized and massively distributed. Particle systems are an approach to swarm intelligence that focus on collective movements, and have been used successfully for applications such as computer animation in graphics and control of movements of autonomous robotic vehicle teams. However, particle system techniques have not been applied substantially to problem solving beyond merely collective navigational tasks.; In this dissertation, I present an extension to particle systems that incorporates top-down, high-level control to self-organizing mobile agents, thereby guiding the self-organizing process and making it possible for particle systems to undertake problem solving directed by goal-oriented behavior while retaining their decentralized, local nature. This extended particle system approach is critically evaluated through three experimental studies that are adapted from well-known problems in multi-agent systems: search and collect, cooperative transport and logistics. The results provide evidence that extended particle systems are capable of exhibiting behavior important for distributed problem solving, such as cooperative sensing, division of labor, sharing of information, and developing global strategies through local interactions. They also show that aggregated movements can be utilized to create coordination at different levels and phases of the performance of a task, whether those include navigation or not, making extended particle systems a useful tool in the construction of adaptive distributed systems.
机译:近年来,研究人员对群体智能作为自适应分布式问题解决的一种有前途的方法表现出了越来越浓厚的兴趣。群智能包括受自然启发的技术,特别是社交昆虫和动物聚集,甚至是人类互动。它们基于自我组织(系统的整体行为来自其相对简单的组件之间的局部交互作用),并且通常是分散的和大规模分布的。粒子系统是一种集中于集体运动的群体智能方法,已成功用于诸如图形中的计算机动画和自动机器人车队的运动控制之类的应用。但是,粒子系统技术并没有被广泛地应用到解决问题上,而不仅仅是集体导航任务。在本文中,我提出了对粒子系统的扩展,该方法将自上而下的高级控制结合到自组织移动代理中,从而指导自组织过程,并使粒子系统有可能进行目标导向的问题解决。导向的行为,同时保留其分散的本地性。通过三项实验研究对这种扩展的粒子系统方法进行了严格的评估,这些研究针对多主体系统中的已知问题进行了改编:搜索和收集,合作运输和物流。结果提供了证据,证明扩展的粒子系统能够表现出对分布式问题解决重要的行为,例如协作感知,分工,信息共享以及通过局部交互作用制定全局策略。他们还表明,聚集的运动可用于在任务执行的不同级别和阶段(无论是否包括导航)创建协调,从而使扩展粒子系统成为构建自适应分布式系统的有用工具。

著录项

  • 作者

    Rodriguez, Alejandro.;

  • 作者单位

    University of Maryland, College Park.$bComputer Science.;

  • 授予单位 University of Maryland, College Park.$bComputer Science.;
  • 学科 Artificial Intelligence.; Computer Science.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 158 p.
  • 总页数 158
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;自动化技术、计算机技术;
  • 关键词

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