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

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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.

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Authors and Affiliations

  1. Research and Development, Primal Innovation, LLC, Sanford, FL, USA

    Gary Stein

  2. Intelligent Systems laboratory, University of Central Florida, Orlando, FL, USA

    Avelino J. Gonzalez

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  1. Gary Stein
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  2. Avelino J. Gonzalez
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Correspondence to Avelino J. Gonzalez.

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Stein, G., Gonzalez, A.J. Learning in context: enhancing machine learning with context-based reasoning. Appl Intell 41, 709–724 (2014). https://doi.org/10.1007/s10489-014-0550-0

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  • DOI: https://doi.org/10.1007/s10489-014-0550-0

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