Constraint Satisfaction for Learning Hypotheses in Inductive Logic Programming

Abstract

Inductive logic programming is a subfield of machine learning which uses first-order logic as a uniform representation of examples, background knowledge, and hypotheses. In many works, it is assumed that examples are clauses and the goal is to find a consistent hypothesis H, that is, a clause entailing all positive examples and no negative example.

We apply constraint satisfaction to learn hypotheses in ILP.

Keywords

• Constraint Satisfaction
• Learning Hypotheses
• Inductive Logic Programming