Learning and Intelligent Systems: Agile Procedural Learning Systems

学习研究表明，人们可以将学习分为两大类：陈述性学习（例如，对事实的口头陈述）和程序性学习（例如，如何骑自行车）。本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对动物和机器人的研究结合起来，以探索一种受生物学启发的敏捷程序学习系统（apl）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，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模式或摄食运动以迅速应对新情况。将受益于敏捷程序学习理论的工程系统示例包括机器人和制造工作单元。特别考虑了敏捷制造的应用领域。敏捷制造工作单元的关键要求是，它能够灵活、快速地适应不断变化的装配线需求、可感知的错误和新任务。尽管机械手变得更灵巧，传感器变得更精确，处理器变得更快，内存变得更便宜，软件变得更容易编写和维护，但真正能够学习和展示智能行为的敏捷工程系统还没有得到证明。衬底不缺；需要一种符合工程原理的理论方法。学习和智能系统（LIS）的自顶向下（认知）和自底向上（反应）方法已经取得了成功，但分别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程序和在轻度变化的环境中执行低级任务的简单代理。相比之下，敏捷程序学习系统在反应性行为和认知技能之间具有中等水平的能力。它们是复杂的、受约束的，并且必须在结构变化的环境中工作。因此，敏捷程序学习的问题直接摆在研究者面前，即建立一个LIS理论，弥合传统的“自上而下”和“自下而上”方法之间的差距。研究人员提出的缺失的“中间中间”理论将采用以下方法进行研究：(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)将这些发现应用于工程实践。更具体地说，他们将探索以下受生物启发的建筑，作为解决敏捷程序学习问题的一种手段：由更高水平的状态和环境依赖电路协调和调节的塑料局部反射电路。研究人员的研究工作有三个核心重点：(1)他们将在具有程序性学习能力的简单动物身上进行实验研究，以确定这种结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代理动态的影响的理解；(3)他们将把中间理论和架构结合到类似动物的机器人和敏捷制造车间中，评估它们在产生学习和智能行为方面的效用，通过改进的性能来衡量。中-外方法产生了关于低层次感觉运动学习和高阶学习的总体科学问题，这些问题只能通过跨学科的方法来回答。将这些工作相互联系起来的重叠任务小组将协同改进每一项工作。

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.