Solution 1 – logic tensor networks

In our first Python NSAI example, we will implement a system based on the Logic Tensor Network (LTN) framework.

In short, LTNs are a sub-class of neural networks that leverage logical propositions (i.e., symbolic logic). LTNs use logical propositions to represent the knowledge base as formulas and deep learning to learn the different weights of these formulas. These logical propositions act as soft constraints on the neural network’s inference. If the neural network’s output violates the logical propositions, then it is penalized. As a result, an LTN during training has two main objectives: 1) satisfy the logical propositions, and 2) improve its predictive performance on the target objective. As such, the logical propositions as model constraints act as a way to directly integrate prior domain knowledge into the neural network.

For the more interested reader, you can read the full LTN paper at https://arxiv.org/pdf/1606...