Title :
A NN algorithm for Boolean satisfiability problems
Author :
Spears, William M.
Author_Institution :
Naval Res. Lab., Washington, DC, USA
Abstract :
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
Keywords :
Boolean functions; Hopfield neural nets; computability; graph theory; Boolean satisfiability problems; GSAT; NNSAT; arbitrary Boolean expression; greedy algorithm; neural network algorithm; truth assignment; Artificial intelligence; Computational complexity; Greedy algorithms; Laboratories; Logic; Neural networks; Operations research; Performance evaluation; Testing; Tree graphs;
Conference_Titel :
Neural Networks, 1996., IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-3210-5
DOI :
10.1109/ICNN.1996.549055
