Conflict-driven Inductive Logic Programming

TL;DR

This paper introduces Conflict-driven ILP (CDILP), a novel approach inspired by SAT/ASP solvers, enabling more scalable learning of answer set programs, especially with noisy data.

Contribution

It formalizes the CDILP approach and presents ILASP3 and ILASP4 systems, significantly improving scalability over previous ILASP versions.

Findings

ILASP3 and ILASP4 outperform earlier systems in scalability.

CDILP effectively handles noisy data.

The approach generalizes to complex ILP tasks.

Abstract

The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of examples. Until recently, most research on ILP targeted learning Prolog programs. The ILASP system instead learns Answer Set Programs (ASP). 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. Early versions of ILASP can be considered meta-level ILP approaches, which encode a learning task as a logic program and delegate the search to an ASP solver. More recently, ILASP has shifted towards a new method, inspired by conflict-driven SAT and ASP solvers. The fundamental idea of the approach, called Conflict-driven ILP (CDILP), is to iteratively interleave the search for a hypothesis with the generation of constraints which explain why the current hypothesis does not cover a particular example. These coverage constraints allow ILASP to rule out not just the current hypothesis, but an entire class of hypotheses that do not satisfy the coverage constraint. This paper formalises the CDILP approach and presents the ILASP3 and ILASP4 systems for CDILP, which are demonstrated to be more scalable than previous ILASP systems, particularly in the presence of noise. Under consideration in Theory and Practice of Logic Programming (TPLP).