The ILASP system for Inductive Learning of Answer Set Programs

Understand

The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of examples in the context of some pre-existing background knowledge.

• Until recently, most research on ILP targeted learning Prolog programs.
• Our own ILASP system instead learns Answer Set Programs, including normal rules, choice rules and hard and weak constraints.
• Learning such expressive programs widens the applicability of ILP considerably; for example, enabling preference learning, learning common-sense knowledge, including defaults and exceptions, and learning non-deterministic theories.