Journal of AI

Kamal¹

Kamalakar²

¹Ou, India

²Ou, USA

inductive logic programming (ILP) is a form of logical machine learning. The goal is to search a hypothesis space for ahypothesis that generalises training examples and background knowledge. We introduce an approach that shrinks thehypothesis space before an ILP system searches it. Our approach uses background knowledge to find rules that cannot be inan optimal hypothesis regardless of the training examples. For instance, our approach discovers relationships such as evennumbers 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-basedILP system. Our experiments on multiple domains, including visual reasoning and game playing, show that our approachcan substantially reduce learning times whilst maintaining predictive accuracies. For instance, given just 10 seconds ofpreprocessing time, our approach can reduce learning times from over 10 hours to only 2 seconds

India, Canada, Pakistan, Bangaladesh