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

自组织粒子系统由许多自主的“粒子”（个体）组成，它们以协调的方式在空间中移动，并受到它们彼此施加的影响的引导。 过去基于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，如一群鸟或一群鱼的运动，以及控制实验机器人团队的运动。本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容量、优先目标、顺序运动模式的库和学习能力。具体目标是创建基准测试问题，用于评估以这种方式扩展的粒子系统，为单个粒子实现目标驱动的控制机制，使粒子能够在解决问题的过程中学习，并应用进化计算方法来创建替代控制机制。 本研究所采用的方法包括计算机仿真、机器学习和进化计算。 主要的预期结果是创建一个多智能体团队，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，这项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向研究自然界自组织过程的生物学家提出建议方面具有重要的实用价值。

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.