An adaptive Inductive Logic Programming system using
Genetic Programming
Evolutionary Programming IV Proceedings of the
Fourth Annual Conference on Evolutionary Programming, page 737--752. San Diego, CA, USA, MIT Press, (1-3 March 1995)Abstract
Recently, there have been increasing interests in
Inductive Logic Programming (ILP) systems. But existing
ILP systems cannot improve themselves automatically.
This paper describes an Adaptive Inductive Logic
Programming (Adaptive ILP) system that evolves during
learning. An adaptive ILP system is composed of an
external interface, a biases base, a knowledge base of
background knowledge, an example database, an empirical
ILP learner, a meta-level learner, and a learning
controller. A preliminary adaptive ILP system has been
implemented. In this implementation, the empirical ILP
learner performs top-down search in the hypothesis
space defined by the concept description language, the
language bias, and the background knowledge. The search
is directed by search biases which can be induced and
refined by genetic programming (Koza 1992).
It has been demonstrated that the adaptive ILP system
performs better than FOIL, a famous ILP system (Quinlan
1990), in inducing logic programs from perfect or noisy
training examples. The experimentation illustrates the
benefit of an adaptive ILP system over existing ILP
systems. The result implies that the search bias
induced by genetic programming (GP) is better than that
of FOIL, which is designed by a top researcher in the
field. Consequently, GP is a promising technique for
implementing a meta-level learning system. The result
is very encouraging as it suggests that the process of
natural selection and evolution can successfully evolve
a high performance ILP system.
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