7,856 research outputs found
Classification of Data to Extract Knowledge from Neural Networks
A major drawback of artificial neural networks is their black-box character. Therefore, the rule
extraction algorithm is becoming more and more important in explaining the extracted rules from the neural
networks. In this paper, we use a method that can be used for symbolic knowledge extraction from neural
networks, once they have been trained with desired function. The basis of this method is the weights of the neural
network trained. This method allows knowledge extraction from neural networks with continuous inputs and
output as well as rule extraction. An example of the application is showed. This example is based on the
extraction of average load demand of a power plant
The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence
Intelligent systems based on first-order logic on the one hand, and on
artificial neural networks (also called connectionist systems) on the other,
differ substantially. It would be very desirable to combine the robust neural
networking machinery with symbolic knowledge representation and reasoning
paradigms like logic programming in such a way that the strengths of either
paradigm will be retained. Current state-of-the-art research, however, fails by
far to achieve this ultimate goal. As one of the main obstacles to be overcome
we perceive the question how symbolic knowledge can be encoded by means of
connectionist systems: Satisfactory answers to this will naturally lead the way
to knowledge extraction algorithms and to integrated neural-symbolic systems.Comment: In Proceedings of INFORMATION'2004, Tokyo, Japan, to appear. 12 page
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Fewer epistemological challenges for connectionism
Seventeen years ago, John McCarthy wrote the note Epistemological challenges for connectionism as a response to Paul Smolensky’s paper 'On the proper treatment of connectionism'. I will discuss the extent to which the four key challenges put forward by McCarthy have been solved, and what are the new challenges ahead. I argue that there are fewer epistemological challenges for connectionism, but progress has been slow. Nevertheless, there is now strong indication that neural-symbolic integration can provide effective systems of expressive reasoning and robust learning due to the recent developments in the field
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