Adaptive Sensory-Motor Planning by Humans and Machines

人类和机器的自适应感觉运动规划

• 批准号: 9024877
• 负责人: Stephen Grossberg
• 金额: $ 32.34万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-07-01 至 1993-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9024877&HistoricalAwards=false
• 关键词: Adaptive; Sensory; Motor; Planning; Humans

This is a renewal of grant IRI-9045939. This project aims at the development of neural network architectures to explain how behaving individuals adapt successfully in real-time to constraints imposed by changing environments. These architectures are capable of automatically adjusting in such a way that high level goals and plans continue to be realized despite changing circumstances. Projects include an analysis of how representations of 3-D space are autonomously learned in real-time. It will address how such spatial representations can be used in a larger system capable of learning to perform skilled arm movement sequences, such as handwriting and visually-guided object manipulation. The project will examine how planned action sequences can be performed with a tool of variable length and mass, such as a pen or hook, without any further learning. It will also study how a working memory can accomplish variable- rate, real-time short term memory storage of temporal order codes, such as codes for rapid speech, typewriting, and eye movement sequences. This project will examine how a self- organizing hierarchy of temporal planning networks can compress the control of complex action sequences. This will lead into how a neural network architecture can learn to control approach toward a valued goal. Finally, the research will address how complementary designs for sensory-cognitive learning systems and cognitive-motor learning systems can be combined into a neural architecture that achieves the benefits of each type of design, without their individual limitations. All of the projects will test the biological relevance of their neural architectures by computer simulations involving large behavioral and neural data bases. They will also provide mathematical and computational analyses of these architectures to characterize circuit designs for adaptive, intelligent machines with new real-time processing capabilities. This project will provide basic insights into various levels of modeling of a user during human-computer interactions.

这是IRI-9045939资助的更新。 该项目旨在 神经网络架构的发展，以解释如何 行为个体成功地实时适应 环境变化带来的限制。 这些 架构能够在这样的环境中自动调整， 继续实现高水平目标和计划的方式 尽管情况不断变化 项目包括分析 如何自主学习三维空间的表示， 实时的 它将解决如何这样的空间表示可以 在一个更大的系统中使用， 手臂运动序列，如手写和视觉引导 对象操作。 该项目将研究计划的行动如何 序列可以用可变长度的工具进行， 质量，如钢笔或钩，没有任何进一步的学习。 它 还将研究工作记忆如何完成可变的- 时间顺序的实时短期存储器 代码，如用于快速语音、打字和眼睛的代码 动作序列 这个项目将研究如何自我- 时间规划网络的组织层次可以压缩 控制复杂的动作序列。 这将导致如何 神经网络架构可以学习控制方法 一个有价值的目标。 最后，研究将解决如何 感官认知学习系统的互补设计， 认知运动学习系统可以结合成一个神经系统， 实现每种设计的优点的架构， 没有他们个人的局限性。 所有项目将 通过以下方式测试其神经架构的生物相关性 包含大量行为和神经数据的计算机模拟 基地 他们还将提供数学和计算 分析这些架构，以表征电路设计 用于具有新的实时处理能力的自适应智能机器 能力的 该项目将提供基本的见解， 在人机交互期间对用户的各种级别的建模 交互.