Metacognition for Self-Regulated Learning in a Dynamic Environment

This paper describes a self-regulated learning system that uses metacognition to decide what to learn, when to learn and how to learn in order to succeed in a dynamic environment. Metacognition provides the system the ability to monitor anomalies and to dynamically change its behavior to fix or work around them. The dynamic environment for the system is an air traffic control domain that has six approach vectors for planes to land. The system has access to three basic approach strategies for choosing a landing terminal: Nearest Terminal, Free Terminal and Queued Terminal. In addition, the system has access to a supervised-learning algorithm that can be used to create new strategies. The system has the ability to generate its own training data sets to train the supervised-learner. The metacognitive component of the system monitors various expectations; anomalies in the environment cause expectation violations. These expectation violations act as indicators for what, when and how to learn. For instance, if an expectation violation occurs because aircraft are not being assigned approach vectors within a given time threshold, the system automatically triggers a change in landing strategies. Examples of anomalies that cause expectation violations include closing one or more of the six approach vectors or changing all of their geographical locations simultaneously. In either case, the system will respond to the situation by assigning the planes to one of the currently active approach vectors.