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Conflict-Driven Inductive Logic Programming

Published online by Cambridge University Press:  09 February 2022

MARK LAW Open the ORCID record for MARK LAW [Opens in a new window]
MARK LAW*
Affiliation:
ILASP Limited, Grantham, UK (e-mail: mark@ilasp.com)
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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 article 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.

Keywords

non-monotonic inductive logic programminganswer set programming conflict driven solving
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 2 , March 2023 , pp. 387 - 414
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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