Paper
De Raedt, L. and Van Laer, W, "Inductive Constraint Logic", in
Proceedings of the 5th Workshop on Algorithmic Lea rning
Theory, Lecture Notes in Artificial Intelligence, Springer Verlag,
1995
[ps format]
Abstract
A novel approach to learning first order logic formulae
from positive and negative examples is presented. Whereas
present inductive logic programming systems employ examples
as true and false ground facts (or clauses), we view examples
as interpretations which are true or false for the target theory.
This viewpoint allows to reconcile
the inductive logic programming paradigm with classical attribute
value learning in the sense that the latter is a special case of the former.
Because of this property, we are able to adapt AQ and CN2 type
algorithms in order to enable learning of full first order formulae.
However, whereas classical learning techniques
have concentrated on concept representations in disjunctive normal form,
we will use a clausal representation, which corresponds to
a conjuctive normal form where each conjunct forms a constraint on
positive examples. This representation duality reverses
also the role of positive and negative examples, both in the heuristics
and in the algorithm. The resulting theory is incorporated in a system named ICL (Inductive
Constraint Logic).