Conflict-driven Inductive Logic Programming
This work addresses the scalability limitations in learning expressive Answer Set Programs for researchers and practitioners in Inductive Logic Programming, particularly in noisy environments.
This paper formalizes Conflict-driven Inductive Logic Programming (CDILP), an approach that iteratively interleaves hypothesis search with constraint generation to explain why a hypothesis fails to cover an example. The resulting ILASP3 and ILASP4 systems demonstrate improved scalability over previous ILASP versions, especially when dealing with noisy data.
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).