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An Integrated Systems Approach to Explanation-Based Conceptual Change

Scott E Friedman,Kenneth D. Forbus

Published 2010 in AAAI Conference on Artificial Intelligence

ABSTRACT

Understanding conceptual change is an important problem in modeling human cognition and in making integrated AI systems that can learn autonomously. This paper describes a model of explanation-based conceptual change, integrating sketch understanding, analogical processing, qualitative models, truth-maintenance, and heuristic-based reasoning within the Companions cognitive architecture. Sketch understanding is used to automatically encode stimuli in the form of comic strips. Qualitative models and conceptual quantities are constructed for new phenomena via analogical reasoning and heuristics. Truth-maintenance is used to integrate conceptual and episodic knowledge into explanations, and heuristics are used to modify existing conceptual knowledge in order to produce better explanations. We simulate the learning and revision of the concept of force, testing the concepts learned via a questionnaire of sketches given to students, showing that our model follows a similar learning trajectory.

PUBLICATION RECORD

  • Publication year

    2010

  • Venue

    AAAI Conference on Artificial Intelligence

  • Publication date

    2010-07-05

  • Fields of study

    Computer Science, Psychology

  • Identifiers
    DOI 10.1609/aaai.v24i1.7572
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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