ITR: Self-Organizing Collective Intelligence for Adaptive Problem-Solving

ITR：用于自适应问题解决的自组织集体智慧

• 批准号: 0325098
• 负责人: James Reggia
• 金额: --
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-10-15 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325098&HistoricalAwards=false
• 关键词: ITR; Self; Organizing; Collective; Intelligence

Self-organizing particle systems consist of numerous autonomous "particles" (individuals) that move through space in a coordinated fashion, guided by influences that they exert on one another. Past computational methods based on such particle systems have primarily been used to produce striking computer animation effects, such as movement of a flock of birds or a school of fish, and to control movements of experimental robot teams.The goal of this project is to extend the methods currently used in particle systems to produce a goal-directed and adaptive collective intelligence. The individual particles have a limited capacity memory, prioritized goals, a repertoire of sequential movement patterns, and the ability to learn. The specific objectives are to create benchmark test problems for evaluating particle systems that have been extended in this fashion, to implement a goal-driven control mechanism for individual particles, to give particles the ability to learn during problem-solving, and to apply evolutionary computation approaches to create alternate control mechanisms. The methods used in this research include computer simulation, machine learning and evolutionary computation. The primary expected result is the creation of a multi-agent team that will function as an effective problem-solving system. In addition to providing insight into the fundamental computational principles underlying this approach to machine intelligence, the results from this research will have substantial practical value in developing natural scene computer animation, guiding the design of multi-robot teams, and suggesting ideas to biologists studying self-organizing processes in nature.

自组织粒子系统由许多自主的“粒子”（个体）组成，它们在相互作用的影响下以协调的方式在空间中移动。过去基于这种粒子系统的计算方法主要用于产生引人注目的计算机动画效果，例如鸟群或鱼群的运动，以及控制实验机器人团队的运动。该项目的目标是扩展目前在粒子系统中使用的方法，以产生目标导向和自适应的集体智能。单个粒子具有有限的记忆能力、优先目标、一系列连续运动模式和学习能力。具体目标是为评估以这种方式扩展的粒子系统创建基准测试问题，为单个粒子实现目标驱动的控制机制，赋予粒子在解决问题过程中学习的能力，并应用进化计算方法来创建替代控制机制。本研究使用的方法包括计算机模拟、机器学习和进化计算。主要的预期结果是创建一个多代理团队，将作为一个有效的解决问题的系统。除了深入了解这种机器智能方法的基本计算原理外，本研究的结果将在开发自然场景计算机动画，指导多机器人团队的设计以及为研究自然界自组织过程的生物学家提供建议方面具有重要的实用价值。