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Logic programs and connectionist networks

✍ Scribed by Pascal Hitzler; Steffen Hölldobler; Anthony Karel Seda


Publisher
Elsevier Science
Year
2004
Tongue
English
Weight
339 KB
Volume
2
Category
Article
ISSN
1570-8683

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✦ Synopsis


One facet of the question of integration of Logic and Connectionist Systems, and how these can complement each other, concerns the points of contact, in terms of semantics, between neural networks and logic programs. In this paper, we show that certain semantic operators for propositional logic programs can be computed by feedforward connectionist networks, and that the same semantic operators for first-order normal logic programs can be approximated by feedforward connectionist networks. Turning the networks into recurrent ones allows one also to approximate the models associated with the semantic operators. Our methods depend on a well-known theorem of Funahashi, and necessitate the study of when Funahashi's theorem can be applied, and also the study of what means of approximation are appropriate and significant.


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Logic programming on a neural network
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We propose a method of doing logic programming on a Hopfield neural network. Optimization of logical consistency is carried out by the network after the connection strengths are defined from the logic program; the network relaxes to neural states corresponding to a valid (or near-valid) interpretati