Abstract
One main difficulty of Inductive Logic Programming lies in learning recursively defined predicates. Today's systems strongly rely on a set of supporting predicates known as the background knowledge, which assumes that the user knows in advance what sort of predicates are required by the target definition. Predicate invention can remedy the situation by extending the specification language with new concepts that appear neither in the examples nor in the background knowledge, and finding a definition for them. A serious concern is that no examples of the invented predicate are explicitly given but rather of the target predicate, so learning has to be done in the absence or scarcity of examples. This work shows an autonomous learning method based on inverting clausal implication that can invent the recursive predicates it needs. The learner is endowed with means to work from a small data set.
On leave from Universidade Federal do Ceará, Brazil. Partially supported by CAPES, Brazil.
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Rios, R., Matwin, S. (1998). Predicate invention from a few examples. In: Mercer, R.E., Neufeld, E. (eds) Advances in Artificial Intelligence. Canadian AI 1998. Lecture Notes in Computer Science, vol 1418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64575-6_70
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DOI: https://doi.org/10.1007/3-540-64575-6_70
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