Abstract
DISCIPLE is a Knowledge Acquisition system that contains several learning mechanisms as recognized by Machine Learning. The central mechanism in DICIPLE is the one of explanations which is used in all the learning modes of DISCIPLE.
When using the Explanation-Based mode of learning, an explanation points at the most relevant features of the examples.
When using the Analogy-Based mode of learning, the explanations are used to generate instances analogous to those provided by the user.
When using the Similarity-Based mode of learning, the explanations are "examples" among which similarities are looked for.
The final result of DISCIPLE is the description of the validity domain of the variables contained in the rules. Since the users always provides totally instantiated rules, the system must automatically variabilize them, and then must find the validity domain of these variables by asking "clever" questions to the user. Given a particular (instantiated) rule by its user, the system will look in its Knowledge Base for possible explanations of this rule, and ask the user to validate them. The set of explanations validated by the user is then used as a set of (almost) sufficient conditions for the application of the instantiated rule.
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Kodratoff, Y., Tecuci, G. (1989). The central role of explanations in disciple. In: Morik, K. (eds) Knowledge Representation and Organization in Machine Learning. Lecture Notes in Computer Science, vol 347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017220
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DOI: https://doi.org/10.1007/BFb0017220
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