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.

Authors

• Sebastian Bader
• Pascal Hitzler
• Steffen H
• ouml; lldobler
• Andreas Witzel

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