Using Constraint Satisfaction for Learning Hypotheses in Inductive Logic Programming
Roman Barták · 2010
Inductive logic programming (ILP) is a subfield of machine learning which uses first-order logic as a uniform representation for examples, background knowledge and hypotheses (Muggleton and De Raedt, 1994). In this paper