It is shown how activation networks can be used for analogical reasoning in the task of ordering the consideration of alternatives. An approach for the integration of the use of an activation network with symbolic processing is described. A prerequisite for this type of integration, as well as for allowing an activation network to use knowledge acquired for or by a knowledge-based system with symbolic processing, is to define a mapping between knowledge of the two types. The authors discuss analogical reasoning, the network structure, its processing, its integration with symbolic processing, learning, and a form of mixed reasoning
Modeling higher order cognitive processes like human decision making come in three representational ...
In this thesis, we discuss different techniques to bridge the gap between two different approaches t...
This thesis addresses the problem of efficiently selecting base cases for problem-solving analogies....
This paper claims that higher cognition implemented by a connectionist system will be essentially an...
This paper claims that higher cognition implemented by a connectionist system will be essentially an...
We consider a class of visual analogical reasoning problems that involve discovering the sequence of...
We present a computational model of a developing system with bounded rationality that is surrounded ...
This paper outlines a theory of analogical reasoning based on a process-model of problem solving by ...
This paper describes an analogy ontology, a formal representation of some key ideas in analogical pr...
Analogical reasoning has a seductive history in artificial intelligence (AI) because of its assumed ...
The goal of neural-symbolic computation is to integrate ro-bust connectionist learning and sound sym...
Intelligent systems based on first-order logic on the one hand, and on artificial neural networks (a...
Analogical Reasoning problems challenge both connectionist and symbolic AI systems as these entail a...
This paper proposes a way of bridging the gap between symbolic and sub-symbolic reasoning. More prec...
Current advances in Artificial Intelligence and machine learning in general, and deep learning in pa...
Modeling higher order cognitive processes like human decision making come in three representational ...
In this thesis, we discuss different techniques to bridge the gap between two different approaches t...
This thesis addresses the problem of efficiently selecting base cases for problem-solving analogies....
This paper claims that higher cognition implemented by a connectionist system will be essentially an...
This paper claims that higher cognition implemented by a connectionist system will be essentially an...
We consider a class of visual analogical reasoning problems that involve discovering the sequence of...
We present a computational model of a developing system with bounded rationality that is surrounded ...
This paper outlines a theory of analogical reasoning based on a process-model of problem solving by ...
This paper describes an analogy ontology, a formal representation of some key ideas in analogical pr...
Analogical reasoning has a seductive history in artificial intelligence (AI) because of its assumed ...
The goal of neural-symbolic computation is to integrate ro-bust connectionist learning and sound sym...
Intelligent systems based on first-order logic on the one hand, and on artificial neural networks (a...
Analogical Reasoning problems challenge both connectionist and symbolic AI systems as these entail a...
This paper proposes a way of bridging the gap between symbolic and sub-symbolic reasoning. More prec...
Current advances in Artificial Intelligence and machine learning in general, and deep learning in pa...
Modeling higher order cognitive processes like human decision making come in three representational ...
In this thesis, we discuss different techniques to bridge the gap between two different approaches t...
This thesis addresses the problem of efficiently selecting base cases for problem-solving analogies....