Extending Answer Set Programs with Neural Networks

TL;DR

This paper introduces NeurASP, a framework that combines answer set programming with neural networks, enabling high-level reasoning on neural network outputs and improving perception accuracy and training quality.

Contribution

NeurASP extends answer set programs with neural networks, allowing complex reasoning and better neural network training through logic-based restrictions.

Findings

NeurASP improves perception accuracy of neural networks.

NeurASP helps train neural networks more effectively.

Training with NeurASP is more time-consuming than pure neural network training.

Abstract

The integration of low-level perception with high-level reasoning is one of the oldest problems in Artificial Intelligence. Recently, several proposals were made to implement the reasoning process in complex neural network architectures. While these works aim at extending neural networks with the capability of reasoning, a natural question that we consider is: can we extend answer set programs with neural networks to allow complex and high-level reasoning on neural network outputs? As a preliminary result, we propose NeurASP -- a simple extension of answer set programs by embracing neural networks where neural network outputs are treated as probability distributions over atomic facts in answer set programs. We show that NeurASP can not only improve the perception accuracy of a pre-trained neural network, but also help to train a neural network better by giving restrictions through logic rules. However, training with NeurASP would take much more time than pure neural network training due to the internal use of a symbolic reasoning engine. For future work, we plan to investigate the potential ways to solve the scalability issue of NeurASP. One potential way is to embed logic programs directly in neural networks. On this route, we plan to first design a SAT solver using neural networks, then extend such a solver to allow logic programs.