@inproceedings{ec418abb831a48be8caafb3fa9c3bcd4,
title = "NeurASP: Embracing neural networks into answer set programming",
abstract = "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.",
author = "Zhun Yang and Adam Ishay and Joohyung Lee",
note = "Funding Information: We are grateful to the anonymous referees for their useful comments. This work was partially supported by the National Science Foundation under Grant IIS-1815337. Publisher Copyright: {\textcopyright} 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.; 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 ; Conference date: 01-01-2021",
year = "2020",
language = "English (US)",
series = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",
pages = "1755--1762",
editor = "Christian Bessiere",
booktitle = "Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020",
}