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
A neural implementation of multi-adjoint logic programming
✍ Scribed by J. Medina; E. Mérida-Casermeiro; M. Ojeda-Aciego
- Publisher
- Elsevier Science
- Year
- 2004
- Tongue
- English
- Weight
- 253 KB
- Volume
- 2
- Category
- Article
- ISSN
- 1570-8683
No coin nor oath required. For personal study only.
✦ Synopsis
A neural net based implementation of propositional [0, 1]-valued multi-adjoint logic programming is presented, which is an extension of earlier work on representing logic programs in neural networks carried out in [A.S. d'Avila Garcez et al.,
📜 SIMILAR VOLUMES
The use o f fuzzy logic to model and manage uncertainty in a rule-based system places high computational demands on an inference engine. In an earlier paper, we introduced trainable neural network structures for fuzzy logic. These networks can learn and extrapolate complex relationships between poss
This special issue of JLP is devoted to high-performance implementations of logic programming systems. The idea of having a special issue was born during a conversation with the editor-in-chief of the Journal of Logic Programming, Maurice Bruynooghe, at the International Logic Programming Symposium