Fassil Molla

Fassil Molla

Hyattsville, Maryland, United States
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To provide innovative robust solutions to challenging problems by applying my experience…

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  • Metacognition for Self-Regulated Learning in a Dynamic Environment

    Institute of Electrical and Electronics Engineers

    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…

    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 expecta-
    tion 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.

    This research has been supported in part by grants from NSF and NASA.

    See publication

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