TY - GEN
T1 - An integrated systems approach to explanation-based conceptual change
AU - Friedman, Scott E.
AU - Forbus, Kenneth D
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77958592531&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:77958592531
SN - 9781577354666
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 1523
EP - 1529
BT - AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference
PB - AI Access Foundation
T2 - 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10
Y2 - 11 July 2010 through 15 July 2010
ER -