Foundations of logic programming
Foundations of logic programming
Connectionism and cognitive architecture: a critical analysis
Connections and symbols
Fractals everywhere
Towards a theory of declarative knowledge
Foundations of deductive databases and logic programming
Neural-Symbolic Learning System: Foundations and Applications
Neural-Symbolic Learning System: Foundations and Applications
Approximating the Semantics of Logic Programs by Recurrent Neural Networks
Applied Intelligence
Connectionist model generation: A first-order approach
Neurocomputing
Learning and Memorizing Models of Logical Theories in a Hybrid Learning Device
Neural Information Processing
Learning from Inconsistencies in an Integrated Cognitive Architecture
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Logics and Networks for Human Reasoning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Extracting reduced logic programs from artificial neural networks
Applied Intelligence
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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.