We want to apply Funahashi\u27s theorem in order to approximate the TP operator for first-order (normal) logic programs P via 3-layer feedforward networks. I.e. we need to understand TP as a continuous function on the reals. We will need to study some preliminaries from set-theoretic topology first result from [HS00, HHS0x], which extends results from [HKS99]. We close with some further considerations about the methods and results
This paper investigates the approximation properties of standard feedforward neural networks (NNs) t...
In this paper, we propose a translation from normal first-order logic programs under the stable mode...
Many computational settings are concerned with finding (all) models of a first-order logic theory fo...
Several recent publications have exhibited relationships between the theories of logic programming a...
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...
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We discuss the computation by neural networks of semantic operators TP determined by propositional l...
AbstractIt is a long-standing and important problem to integrate logic-based systems and connectioni...
Hölldobler and Kalinke showed how, given a propositional logic program P, a 3-layer feedforward arti...
In this paper we extend Lin and Zhao’s notions of loops and loop formulas to normal logic programs t...
Graphs of the single-step operator for first-order logic programs—displayed in the real plane—exhibi...
It is well known that Artificial Neural Networks are universal approximators. The classical result ...
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...
This paper investigates the approximation properties of standard feedforward neural networks (NNs) t...
In this paper, we propose a translation from normal first-order logic programs under the stable mode...
Many computational settings are concerned with finding (all) models of a first-order logic theory fo...
Several recent publications have exhibited relationships between the theories of logic programming a...
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...
Abstract. In this paper, we develop a theory of the integration of fibring neural net-works (a gener...
We discuss the computation by neural networks of semantic operators TP determined by propositional l...
AbstractIt is a long-standing and important problem to integrate logic-based systems and connectioni...
Hölldobler and Kalinke showed how, given a propositional logic program P, a 3-layer feedforward arti...
In this paper we extend Lin and Zhao’s notions of loops and loop formulas to normal logic programs t...
Graphs of the single-step operator for first-order logic programs—displayed in the real plane—exhibi...
It is well known that Artificial Neural Networks are universal approximators. The classical result ...
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...
This paper investigates the approximation properties of standard feedforward neural networks (NNs) t...
In this paper, we propose a translation from normal first-order logic programs under the stable mode...
Many computational settings are concerned with finding (all) models of a first-order logic theory fo...