Honey, I shrunk the hypothesis space (through logical preprocessing)

TL;DR

This paper presents a method to reduce the hypothesis space in inductive logic programming using background knowledge, significantly speeding up learning without sacrificing accuracy.

Contribution

It introduces a logical preprocessing approach that identifies and removes impossible rules from the hypothesis space before learning begins.

Findings

Reduces learning time from over 10 hours to 2 seconds in some cases.

Maintains predictive accuracy despite shrinking the hypothesis space.

Effective across multiple domains including visual reasoning and game playing.

Abstract

Inductive logic programming (ILP) is a form of logical machine learning. The goal is to search a hypothesis space for a hypothesis that generalises training examples and background knowledge. We introduce an approach that 'shrinks' the hypothesis space before an ILP system searches it. Our approach uses background knowledge to find rules that cannot be in an optimal hypothesis regardless of the training examples. For instance, our approach discovers relationships such as "even numbers cannot be odd" and "prime numbers greater than 2 are odd". It then removes violating rules from the hypothesis space. We implement our approach using answer set programming and use it to shrink the hypothesis space of a constraint-based ILP system. Our experiments on multiple domains, including visual reasoning and game playing, show that our approach can substantially reduce learning times whilst maintaining predictive accuracies. For instance, given just 10 seconds of preprocessing time, our approach can reduce learning times from over 10 hours to only 2 seconds.