Solving SAT in linear time with a neural-like membrane system

Authors:
Juan Pazos;Alfonso Rodríguez-Patón;Andrés Silva
Affiliations:
Facultad de Informática, Madrid, Spain;Facultad de Informática, Madrid, Spain;Facultad de Informática, Madrid, Spain
Venue:
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Year:
2003

Citing 4
Cited 2

The Semantics of Predicate Logic as a Programming Language

Journal of the ACM (JACM)
An Efficient Multivalued Hopfield Network for the Traveling Salesman Problem

Neural Processing Letters
Approximating the Semantics of Logic Programs by Recurrent Neural Networks

Applied Intelligence
A Multi-adjoint Logic Approach to Abductive Reasoning

Proceedings of the 17th International Conference on Logic Programming

Inhibiting/de-inhibiting rules in p systems

WMC'04 Proceedings of the 5th international conference on Membrane Computing
On efficient algorithms for SAT

CMC'12 Proceedings of the 13th international conference on Membrane Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A neural net based development of multi-adjoint logic programming is presented. Transformation rules carry programs into neural networks, where truth-values of rules relate to output of neurons, truth-values of facts represent input, and network functions are determined by a set of general operators; the output of the net being the values of propositional variables under its minimal model. Some experimental results are reported.