ML Anthology

A Computational Model of Acquisition for Children's Addtion Strategies

Randolph M. Jones, Kurt VanLehn
ICML 1991 pp. 65-69
doi:10.1016/B978-1-55860-200-7.50017-9 /icml/1991/jones1991icml-computational/

Abstract

GlPS is a problem-solving system that models the strategy shifts of children learning to add. The system uses a generalized form of means-ends analysis as its reasoning algorithm, and it learns probabilistic selection and execution concepts for its operators. With this combination, GIPS models the “SUM-to-MIN― transition that children exhibit when learning to add (Siegler & Jenkins, 1989). The system generates the appropriate final strategy, as well as the intermediate strategies that Siegler and Jenkins observed.

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Cite

Text

Jones and VanLehn. "A Computational Model of Acquisition for Children's Addtion Strategies." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50017-9

Markdown

[Jones and VanLehn. "A Computational Model of Acquisition for Children's Addtion Strategies." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/jones1991icml-computational/) doi:10.1016/B978-1-55860-200-7.50017-9

BibTeX

@inproceedings{jones1991icml-computational,
  title     = {{A Computational Model of Acquisition for Children's Addtion Strategies}},
  author    = {Jones, Randolph M. and VanLehn, Kurt},
  booktitle = {International Conference on Machine Learning},
  year      = {1991},
  pages     = {65-69},
  doi       = {10.1016/B978-1-55860-200-7.50017-9},
  url       = {https://mlanthology.org/icml/1991/jones1991icml-computational/}
}