Add to ORKGWe 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
Matriculation Number: 3065439 The integration of the paradigms of logic programs and connectionist s...
The goal of this article is to construct a connectionist inference engine that is capable of represe...
Graphs of the single-step operator for first-order logic programs—displayed in the real plane—exhibi...
We present a fully connectionist system for the learning of first-order logic programs and the gener...
We present a fully connectionist system for the learning of first-order logic programs and the gener...
One facet of the question of integration of Logic and Connectionist Systems, and how these can compl...
AbstractOne facet of the question of integration of Logic and Connectionist Systems, and how these c...
Knowledge based artificial networks networks have been applied quite successfully to propositional k...
We discuss the computation by neural networks of semantic operators TP determined by propositional l...
The integration of symbolic and neural-network-based artificial intelligence paradigms constitutes ...
The integration of symbolic and neural-network-based artificial intelligence paradigms constitutes a...
Intelligent systems based on first-order logic on the one hand, and on artificial neural networks (a...
AbstractIt is a long-standing and important problem to integrate logic-based systems and connectioni...
The integration of symbolic and neural-network-based artificial intelligence paradigms constitutes a...
AbstractThe paper presents a connectionist framework that is capable of representing and learning pr...
Matriculation Number: 3065439 The integration of the paradigms of logic programs and connectionist s...
The goal of this article is to construct a connectionist inference engine that is capable of represe...
Graphs of the single-step operator for first-order logic programs—displayed in the real plane—exhibi...
We present a fully connectionist system for the learning of first-order logic programs and the gener...
We present a fully connectionist system for the learning of first-order logic programs and the gener...
One facet of the question of integration of Logic and Connectionist Systems, and how these can compl...
AbstractOne facet of the question of integration of Logic and Connectionist Systems, and how these c...
Knowledge based artificial networks networks have been applied quite successfully to propositional k...
We discuss the computation by neural networks of semantic operators TP determined by propositional l...
The integration of symbolic and neural-network-based artificial intelligence paradigms constitutes ...
The integration of symbolic and neural-network-based artificial intelligence paradigms constitutes a...
Intelligent systems based on first-order logic on the one hand, and on artificial neural networks (a...
AbstractIt is a long-standing and important problem to integrate logic-based systems and connectioni...
The integration of symbolic and neural-network-based artificial intelligence paradigms constitutes a...
AbstractThe paper presents a connectionist framework that is capable of representing and learning pr...
Matriculation Number: 3065439 The integration of the paradigms of logic programs and connectionist s...
The goal of this article is to construct a connectionist inference engine that is capable of represe...
Graphs of the single-step operator for first-order logic programs—displayed in the real plane—exhibi...