Learning in context: enhancing machine learning with context-based reasoning

    Authors

    G. Stein;A. J. Gonzalez

    Comments

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    Abbreviated Journal Title

    Appl. Intell.

    Keywords

    Machine learning from observation; Context-based reasoning; Learning; from demonstration; Learning from imitation; DIVIDE-AND-CONQUER; PARTICLE SWARM OPTIMIZATION; HUMAN-PERFORMANCE; NEURAL NETWORKS; BEHAVIOR; DISCOVERY; Computer Science, Artificial Intelligence

    Abstract

    This article describes how an experiment to train an agent to perform a task, which had originally failed, was made successful by incorporating a contextual structure that decomposed the tasks into contexts through Context-based Reasoning. The task involved a simulation of a crane that was used by a human operator to move boxes from arbitrary locations throughout a wide area to a designated drop off location in the environment. Initial attempts to teach an agent how to perform the task through observation in a context-free manner yielded poor performance. However, when the task to be learned was decomposed into separate contexts and the agents learned each context independently, the performance improved significantly. The paper describes the process that enabled the improvements achieved and discusses the tests and results that demonstrated the improvement.

    Journal Title

    Applied Intelligence

    Volume

    41

    Issue/Number

    3

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    709

    Last Page

    724

    WOS Identifier

    WOS:000342426700004

    ISSN

    0924-669X

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