Efficient rule induction by ignoring pointless rules
This work addresses efficiency bottlenecks in ILP for domains such as visual reasoning and game playing, representing an incremental improvement.
The paper tackles the problem of inductive logic programming (ILP) by introducing an approach that identifies and ignores pointless rules, which contain redundant literals or cannot discriminate against negative examples, resulting in a 99% reduction in learning times while maintaining predictive accuracies across domains like visual reasoning and game playing.
The goal of inductive logic programming (ILP) is to find a set of logical rules that generalises training examples and background knowledge. We introduce an ILP approach that identifies pointless rules. A rule is pointless if it contains a redundant literal or cannot discriminate against negative examples. We show that ignoring pointless rules allows an ILP system to soundly prune the hypothesis space. Our experiments on multiple domains, including visual reasoning and game playing, show that our approach can reduce learning times by 99% whilst maintaining predictive accuracies.