NeurASP: Embracing Neural Networks into Answer Set Programming
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 1755-1762. https://doi.org/10.24963/ijcai.2020/243
We present NeurASP, a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts in answer set programs, NeurASP provides a simple and effective way to integrate sub-symbolic and symbolic computation. We demonstrate how NeurASP can make use of a pre-trained neural network in symbolic computation and how it can improve the neural network's perception result by applying symbolic reasoning in answer set programming. Also, NeurASP can make use of ASP rules to train a neural network better so that a neural network not only learns from implicit correlations from the data but also from the explicit complex semantic constraints expressed by the rules.
Knowledge Representation and Reasoning: Knowledge Representation Languages
Machine Learning: Neuro-Symbolic Methods