Applied Partial Constraint Satisfaction Using Weighted Iterative Repair
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Domain-independent extensions to GSAT: solving large structured satisfiability problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Hidden gold in random generation of SAT satisfiable instances
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Performance test of local search algorithms using new types of random CNF formulas
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Adding new clauses for faster local search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Weighting for godot: learning heuristics for GSAT
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Adaptive Optimizing Compilers for the 21st Century
The Journal of Supercomputing
Integer optimization by local search: a domain-independent approach
Integer optimization by local search: a domain-independent approach
| Hi-index | 0.00 |
One of the surprising findings from the study of CNF satisfiability in the 1990's has been the success of iterative repair techniques, and in particular of weighted iterative repair. However, attempts to improve weighted iterative repair have either produced marginal benefits or rely on domain specific heuristics. This paper introduces a new extension of constraint weighting called Arc Weighting Iterative Repair, that is applicable outside the CNF domain and can significantly improve the perfonnance of constraint weighting. The new weighting strategy extends constraint weighting by additionally weighting the connections or arcs between constraints. These arc weights represent increased knowledge of the search space and can be used to guide the search more efficiently. The main aim of the research is to develop an arc weighting algorithm that creates more benefit than overhead in reducing moves in the search space. Initial empirical tests indicate the algorithm does reduce search steps and times for a selection ofCNF and CSP problems.