Knowledge Refactoring for Inductive Program Synthesis

Authors

  • Sebastijan Dumancic KU Leuven, Belgium
  • Tias Guns KU Leuven, Belgium
  • Andrew Cropper Oxford University, United Kingdom

Keywords:

Relational Learning, Constraint Optimization

Abstract

Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introduce the knowledge refactoring problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it. We focus on inductive logic programming, where the knowledge base is a logic program. We introduce Knorf, a system which solves the refactoring problem using constraint optimisation. A key feature of Knorf is that, rather than simply removing knowledge, it also introduces new knowledge through predicate invention. We evaluate our approach on two domains: building Lego structures and real-world string transformations. Our experiments show that learning from refactored knowledge can improve predictive accuracies fourfold and reduce learning times by half.

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Published

2021-05-18

How to Cite

Dumancic, S., Guns, T., & Cropper, A. (2021). Knowledge Refactoring for Inductive Program Synthesis. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 7271-7278. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16893

Issue

Section

AAAI Technical Track on Machine Learning I