Inductive Learning of Answer Set Programs from Noisy Examples

TL;DR

This paper introduces a noise-tolerant framework for learning answer set programs in non-monotonic inductive logic programming, demonstrating improved accuracy of the ILASP3 system on various datasets.

Contribution

It presents a novel noise-tolerant generalisation of the answer set learning framework and evaluates ILASP3, showing superior performance over existing ILP systems.

Findings

ILASP3 achieves higher accuracy on synthetic datasets.

ILASP3 outperforms previous ILP systems and differentiable frameworks.

The new framework effectively handles noisy data in answer set learning.

Abstract

In recent years, non-monotonic Inductive Logic Programming has received growing interest. Specifically, several new learning frameworks and algorithms have been introduced for learning under the answer set semantics, allowing the learning of common-sense knowledge involving defaults and exceptions, which are essential aspects of human reasoning. In this paper, we present a noise-tolerant generalisation of the learning from answer sets framework. We evaluate our ILASP3 system, both on synthetic and on real datasets, represented in the new framework. In particular, we show that on many of the datasets ILASP3 achieves a higher accuracy than other ILP systems that have previously been applied to the datasets, including a recently proposed differentiable learning framework.