Repairing incorrect knowledge with model formulation and metareasoning

Scott E. Friedman, Kenneth D Forbus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Learning concepts via instruction and expository texts is an important problem for modeling human learning and for making autonomous AI systems. This paper describes a computational model of the self-explanation effect, whereby conceptual knowledge is repaired by integrating and explaining new material. Our model represents conceptual knowledge with compositional model fragments, which are used to explain new material via model formulation. Preferences are computed over explanations and conceptual knowledge, along several dimensions. These preferences guide knowledge integration and question-answering. Our simulation learns about the human circulatory system, using facts from a circulatory system passage used in a previous cognitive psychology experiment. We analyze the simulation's perfo rmance, showing that individual differences in sequences of models learned by students can be explained by different parameter settings in our model.

Original languageEnglish (US)
Title of host publicationIJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
Pages887-893
Number of pages7
DOIs
StatePublished - Dec 1 2011
Event22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia, Spain
Duration: Jul 16 2011Jul 22 2011

Other

Other22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
CountrySpain
CityBarcelona, Catalonia
Period7/16/117/22/11

ASJC Scopus subject areas

  • Artificial Intelligence

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    Friedman, S. E., & Forbus, K. D. (2011). Repairing incorrect knowledge with model formulation and metareasoning. In IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence (pp. 887-893) https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-154