This paper is about how to combine two different types of artificial intelligence (AI): neural networks and symbolic reasoning. Neural networks are really good at recognizing patterns, like identifying objects in a picture. Symbolic reasoning is good at understanding relationships and logic, like figuring out the rules of a game. The authors of this paper explore different ways to connect these two types of AI so they can work together. One way is to use the neural network to identify patterns, and then use symbolic reasoning to make decisions based on those patterns. For example, a neural network could identify the pieces on a chessboard, and then symbolic reasoning could use the rules of chess to figure out the best move. Another way is to use symbolic reasoning to help train the neural network. For example, if we know that humans are mammals, we can use that knowledge to help a neural network learn to classify animals. The paper discusses the benefits and challenges of each approach, and it concludes that combining neural networks and symbolic reasoning is a promising way to create more powerful and explainable AI systems.

https://arxiv.org/pdf/2410.22077