Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples

TL;DR

This paper introduces a scalable, sound, and complete algorithm for learning answer set programs from distinct examples, enabling application to large datasets like bAbl and MNIST, which was previously challenging.

Contribution

The authors propose a novel, efficient algorithm for inductive logic programming that improves scalability and control, allowing learning from large, complex datasets.

Findings

Successfully learned answer set programs from bAbl and MNIST datasets

Demonstrated improved scalability over previous ILP methods

Provided a publicly available implementation for broader use

Abstract

Over the years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the Inductive Logic Programming algorithms, have often failed at the task due to the vastness of these datasets. This has impacted the usability of knowledge representation and reasoning techniques in the development of AI systems. In this research, we try to address this scalability issue for the algorithms that learn answer set programs. We present a sound and complete algorithm which takes the input in a slightly different manner and performs an efficient and more user controlled search for a solution. We show via experiments that our algorithm can learn from two popular datasets from machine learning community, namely bAbl (a question answering dataset) and MNIST (a dataset for handwritten digit recognition), which to the best of our knowledge was not previously possible. The system is publicly available at https://goo.gl/KdWAcV. This paper is under consideration for acceptance in TPLP.