NeurASP: Embracing Neural Networks into Answer Set Programming
This work addresses the problem of combining sub-symbolic and symbolic AI for researchers in AI and logic programming, offering a novel integration method.
The authors tackled the integration of neural networks with symbolic reasoning by introducing NeurASP, which treats neural network outputs as probability distributions in answer set programs, enabling improved perception results and better training through explicit semantic constraints.
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 be used to train a neural network better by training with ASP rules 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.