Name
Abstract | ||
|---|---|---|
Considerable progress has recently been made in using clause weighting algorithms such as DLM and SDF to solve SAT benchmark problems. While these algorithms have outperformed earlier stochastic techniques on many larger problems, this improvement has been bought at the cost of extra parameters and the complexity of fine tuning these parameters to obtain optimal run-time performance. This paper examines the use of parameters, specifically in relation to DLM, to identify underlying features in clause weighting that can be used to eliminate or predict workable parameter settings. To this end we propose and empirically evaluate a simplified clause weighting algorithm that replaces the tabu list and flat moves parameter used in DLM. From this we show that our simplified clause weighting algorithm is competitive with DLM on the four categories of SAT problem for which DLM has already been optimised. |
Year | Venue | Keywords |
|---|---|---|
2002 | Australian Joint Conference on Artificial Intelligence | considerable progress,larger problem,sat problem,extra parameter,sat clause,optimal run-time performance,flat move,clause weighting algorithm,clause weighting,towards fewer parameters,workable parameter setting,sat benchmark problem |
Field | DocType | Volume |
Constraint satisfaction,Weighting,Computer science,Fine-tuning,Boolean satisfiability problem,Sat problem,Propositional calculus,Algorithm,Artificial intelligence,Machine learning | Conference | 2557 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-00197-2 | 6 |
PageRank | References | Authors |
0.54 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Thornton | 1 | 6 | 0.54 |
| Wayne J. Pullan | 2 | 232 | 12.73 |
| Justin Terry | 3 | 6 | 0.54 |