Learning and Intelligent Systems: Agile Procedural Learning Systems

学习的研究表明，人们可以定义两大类：陈述性学习（例如，事实的口头陈述）和程序学习（例如，如何骑自行车（How to Ride a Bicycle） 这项研究旨在阐明程序学习的原则，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联合收割机结合起来，以探索敏捷过程学习系统（APLS）的生物启发架构。 现有的敏捷过程学习系统包括人类和动物。 甚至更简单的动物，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或进食运动的模式，以迅速应对新的情况。 受益于敏捷过程学习理论的示例工程系统包括机器人和制造工作单元。 特别是考虑到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、感知错误和新任务。 尽管机械手的速度越来越快，传感器的精度越来越高，处理器的速度越来越快，内存越来越便宜，软件也越来越容易编写和维护，但真正能够学习和表现出智能行为的敏捷工程系统还没有得到证明。 并不缺乏基础;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）的学习和智能系统（LIS）的方法取得了成功，但只有高度复杂的程序执行高层次的任务，在高度结构化的环境和简单的代理执行低层次的任务，在温和变化的环境中，分别。 相比之下，敏捷过程式学习系统具有介于反应行为和认知技能之间的中等能力。 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，敏捷过程学习的问题直接面对研究者建立一个理论的LIS，桥梁之间的差距传统的“自上而下”和“自下而上”的方法差距。 研究人员提出的缺失的“中间出”理论将采用以下方法：（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，他们将探索以下生物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依赖电路协调和调制的可塑性局部反射电路。 研究人员的研究工作有三个核心目标：（1）他们将在能够进行程序学习的简单动物中进行实验研究，以确定这种结构实际上是如何用于学习任务的;（2）他们将开发模型和数学工具，将这些发现形式化，使综合符号反应理论成为可能，提高对学习和环境对智能自主代理动态的影响的理解;（3）他们将把中间出理论和架构结合到动物机器人和敏捷制造工作单元中，评估他们在产生学习和智能行为方面的效用，通过改善的表现来衡量。 中间出的方法产生了关于低级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。将这些努力相互联系起来的重叠工作队将协同改善每一项努力。

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.