A Fully Connectionist Model Generator for Covered First-Order Logic Programs
We present a fully connectionist system for the learning of first-order logic programs and the generation of corresponding models: Given a program and a set of training examples, we embed the associated semantic operator into a feed-forward network and train the network using the examples. This results in the learning of first-order knowledge while damaged or noisy data is handled gracefully
|Logistics (theme 3)|
|International Joint Conference on Artificial Intelligence|
|Organisation||Networks and Optimization|
Bader, S, Hitzler, P, Hölldobler, S, & Witzel, S. A. (2007). A Fully Connectionist Model Generator for Covered First-Order Logic Programs. In M.M Veloso (Ed.), Proceedings of the 20th International Joint Conference on Artificial Intelligence (pp. 666–671). AAAI.