Abstract
There are many systems that learn by creating an explanation for some phenomenon. They draw upon a domain theory in order to generate the explanation. One approach to acquiring that domain theory is to analyze phenomena that can’t be explained using the current domain theory. Any piece of knowledge that would complete the explanation is a candidate for addition to the domain theory. By examining several phenomena, the pieces of knowledge that have the most explanatory power can be located and added to the domain theory. A system based on this learning technique has successfully learned certain basic mathematical skills.
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© 1986 Kluwer Academic Publishers
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VanLehn, K. (1986). Learning a Domain Theory by Completing Explanations. In: Machine Learning. The Kluwer International Series in Engineering and Computer Science, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-2279-5_72
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DOI: https://doi.org/10.1007/978-1-4613-2279-5_72
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4612-9406-1
Online ISBN: 978-1-4613-2279-5
eBook Packages: Springer Book Archive