A NN algorithm for Boolean satisfiability problems

Satisfiability (SAT) refers to the task of finding a truth assignment that makes an arbitrary Boolean expression true. This paper compares a neural network algorithm (NNSAT) with GSAT, a greedy algorithm for solving satisfiability problems. GSAT can solve problem instances that are difficult for tra...

Full description

Saved in:
Bibliographic Details
Published in1996 IEEE International Conference on Neural Networks Vol. 2; pp. 1121 - 1126 vol.2
Main Author Spears, W.M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1996
Subjects
Artificial intelligence
Computational complexity
Greedy algorithms
Laboratories
Logic
Neural networks
Operations research
Performance evaluation
Testing
Tree graphs
Online AccessGet full text
ISBN0780332105
9780780332102
DOI10.1109/ICNN.1996.549055

Cover

More Information
Summary:Satisfiability (SAT) refers to the task of finding a truth assignment that makes an arbitrary Boolean expression true. This paper compares a neural network algorithm (NNSAT) with GSAT, a greedy algorithm for solving satisfiability problems. GSAT can solve problem instances that are difficult for traditional satisfiability algorithms. Results suggest that NNSAT scales better as the number of variables increase, solving at least as many hard SAT problems.
ISBN:0780332105
9780780332102
DOI:10.1109/ICNN.1996.549055