It has been one of the great challenges of neuro-symbolic integration to represent recursive logic programs using neural networks of finite size. In this paper, we propose to implement neural networks that can process recursive programs viewed as inductive definitions.</p
Logic program and neural networks are two important perspectives in artificial intelligence. The maj...
topic: The research field of neurosymbolic integration aims at combining the advantages of neural ne...
Hölldobler and Kalinke showed how, given a propositional logic program P, a 3-layer feedforward arti...
In this paper we show that programming languages can be translated into recurrent (analog, rational ...
Neuro-Symbolic Integration is an interdisciplinary area that endeavours to unify neural networks and...
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...
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...
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...
Is thought computation over ideas? Turing, and many cognitive scientists since, have assumed so, and...
Several recent publications have exhibited relationships between the theories of logic programming a...
Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can l...
"Artificial neural networks" provide an appealing model of computation. Such networks consist of an ...
Logic program and neural networks are two important perspectives in artificial intelligence. The maj...
topic: The research field of neurosymbolic integration aims at combining the advantages of neural ne...
Hölldobler and Kalinke showed how, given a propositional logic program P, a 3-layer feedforward arti...
In this paper we show that programming languages can be translated into recurrent (analog, rational ...
Neuro-Symbolic Integration is an interdisciplinary area that endeavours to unify neural networks and...
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...
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...
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...
Is thought computation over ideas? Turing, and many cognitive scientists since, have assumed so, and...
Several recent publications have exhibited relationships between the theories of logic programming a...
Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can l...
"Artificial neural networks" provide an appealing model of computation. Such networks consist of an ...
Logic program and neural networks are two important perspectives in artificial intelligence. The maj...
topic: The research field of neurosymbolic integration aims at combining the advantages of neural ne...
Hölldobler and Kalinke showed how, given a propositional logic program P, a 3-layer feedforward arti...