Learning and Intelligent Systems: Agile Procedural Learning Systems

学习和智能系统：敏捷程序学习系统

• 批准号: 9720309
• 负责人: Randall Beer
• 金额: $ 77.5万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-10-01 至 2001-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9720309&HistoricalAwards=false
• 关键词: Learning; Intelligent; Systems; Agile; Procedural

Studies of learning indicate that one can define two broad categories: declarative learning (e.g., verbal statements of fact) and procedural learning (e.g., how to ride a bicycle). This research aims to clarify principles of procedural learning, and thus have an impact on understanding how to train people or machines to accomplish complex sensorimotor tasks in changing environments. The researcher's goal is to combine studies on animals and robots in order to explore a biologically-inspired architecture for agile procedural learning systems (APLS). Existing agile procedural learning systems include humans and animals. Even simpler animals, such as insects or mollusks, show a remarkable agility in solving survival problems. After encountering novel terrain, or novel food, these animals can adjust patterns of body or ingestive movements to handle the new situation rapidly. Example engineering systems that would benefit from a theory of agile procedural learning include robots and manufacturing workcells. In particular, considering the application domain of agile manufacturing. The key requirement of an agile manufacturing workcell is that it be capable of flexibly and rapidly adjusting to changing assembly line demands, perceived errors, and new tasks. Though manipulators have gotten more dextrous and sensors more accurate, though processors have gotten faster, memory cheaper, and software easier to write and maintain, truly agile engineering systems that learn and exhibit intelligent behavior have not been demonstrated. The substrate is not lacking; a theoretical approach yielding engineering principles is needed. Top-down (cognitive) and bottom-up (reactive) approaches to learning and intelligent systems (LIS) have yielded successes, but only for highly-complex programs performing high-level tasks in highly-structured environments and for simple agents performing low-level tasks in mildly-changing environments, respectively. In contrast, agile procedural learning systems possess a middle-level competence between reactive behavior and cognitive skills. They are complex, constrained, and must work in environments whose structure changes. Thus, the problem of agile procedural learning confronts the investigators directly with building a theory of LIS that bridges the gap between the traditional "top down" and "bottom up" approaches. The missing "middle out" theory the investigators propose will be pursued with the following approach: (1) identify principles used in existing agile procedural teaming systems; (2) develop models and mathematical tools that bridge the gap between "top-down" and "bottom-up" methods; and (3) transfer these findings to engineering practice. More specifically, they will explore the following biologically-inspired architecture as a means of solving the problem of agile procedural learning: plastic local reflex circuitry coordinated and comodulated by higher-level state- and environment-dependent circuits. The investigators research effort has three core thrusts: (1) they will pursue experimental studies in simpler animals that are capable of procedural learning to determine how this architecture is actually used for learning tasks; (2) they will develop models and mathematical tools that formalize these findings, make an integrative symbolic-reactive theory possible, and improve understanding of the influence that learning and environment have on the dynamics of intelligent autonomous agents; and (3) they will incorporate both into animal-like robots and an agile manufacturing workcell the middle-out theory and architecture, evaluating their utility in producing learning and intelligent behavior, measured by improved performance. The middle-out approach generates overarching scientific questions about low-level sensorimotor learning and higher-order learning that can only be answered using an interdisciplinary approach. Overlapping task teams interlinking these efforts will synergistically improve each of them.

学习研究表明，可以定义两大类：陈述性学习（例如，口头陈述事实）和程序性学习（例如，如何骑自行车）。 这项研究旨在阐明程序学习的原理，从而对理解如何训练人或机器在不断变化的环境中完成复杂的感觉运动任务产生影响。 研究人员的目标是将动物和机器人的研究结合起来，探索敏捷程序学习系统 (APLS) 的受生物学启发的架构。 现有的敏捷程序学习系统包括人类和动物。 即使是更简单的动物，例如昆虫或软体动物，在解决生存问题时也表现出非凡的敏捷性。 在遇到新的地形或新的食物后，这些动物可以调整身体或摄取运动的模式以快速应对新情况。 受益于敏捷程序学习理论的示例工程系统包括机器人和制造工作单元。 特别是考虑敏捷制造的应用领域。 敏捷制造工作单元的关键要求是能够灵活、快速地适应不断变化的装配线需求、感知到的错误和新任务。 尽管操纵器变得更加灵巧，传感器更加准确，处理器变得更快，内存更便宜，软件更易于编写和维护，但能够学习和表现出智能行为的真正敏捷的工程系统尚未得到证实。 底材不缺；需要一种产生工程原理的理论方法。 自上而下（认知）和自下而上（反应性）的学习和智能系统（LIS）方法已经取得了成功，但仅适用于在高度结构化的环境中执行高级任务的高度复杂的程序，以及在温和变化的环境中执行低级任务的简单代理。 相比之下，敏捷程序学习系统拥有介于反应行为和认知技能之间的中等能力。 它们很复杂、受到限制，并且必须在结构发生变化的环境中工作。 因此，敏捷程序学习的问题使研究者直接面临建立 LIS 理论的问题，该理论弥合了传统“自上而下”和“自下而上”方法之间的差距。 调查人员提出的缺失的“中出”理论将通过以下方法来实现：（1）确定现有敏捷程序团队系统中使用的原则； (2) 开发模型和数学工具，弥合“自上而下”和“自下而上”方法之间的差距； (3)将这些发现转化为工程实践。 更具体地说，他们将探索以下受生物学启发的架构作为解决敏捷程序学习问题的一种手段：由更高级别的状态和环境相关电路协调和共调制的塑料局部反射电路。 研究人员的研究工作有三个核心主旨：（1）他们将在更简单的动物中进行实验研究，这些动物能够进行程序学习，以确定这种架构如何实际用于学习任务； （2）他们将开发模型和数学工具来形式化这些发现，使综合符号反应理论成为可能，并提高对学习和环境对智能自主体动态影响的理解； （3）他们将把中出理论和架构纳入类动物机器人和敏捷制造工作单元中，评估它们在产生学习和智能行为方面的效用，并通过改进的性能来衡量。 由中而外的方法产生了有关低水平感觉运动学习和高阶学习的总体科学问题，这些问题只能使用跨学科的方法来回答。将这些工作相互联系的重叠任务团队将协同改进每一项工作。