Year: 1992
Type: conference-paper
Abstract: The field of Inductive Logic Programming (ILP) is concerned with inducing logic programs from examples in the presence of background knowledge. This paper defines the ILP problem, and describes the various syntactic restrictions that are commonly used for learning first-order representations. We then derive some positive results concerning the learnability of these restricted classes of logic programs, by reduction to a standard propositional learning problem. More specifically, k-clause predicate definitions consisting of determinate, function-free, non-recursve Horn clauses with variables of bounded depth are polynomially learnable under simple distributions. Similarly, recursive k-clause definitions are polynomially learnable under simple distributions if we allow existential and membership queries about the target concept.
Institution: Turing Institute
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: National Science Foundation
OpenAlex ID: W2004683093