Dual viewpoint heuristics for binary constraint satisfaction problems
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
New methods to color the vertices of a graph
Communications of the ACM
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
VARSAT: Integrating Novel Probabilistic Inference Techniques with DPLL Search
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Optimal refutations for constraint satisfaction problems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Sampling strategies and variable selection in weighted degree heuristics
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
CSCLP'09 Proceedings of the 14th Annual ERCIM international conference on Constraint solving and constraint logic programming
A hyperheuristic approach for dynamic enumeration strategy selection in constraint satisfaction
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Analysis of heuristic synergies
CSCLP'05 Proceedings of the 2005 Joint ERCIM/CoLogNET international conference on Constraint Solving and Constraint Logic Programming
Complexity analysis of heuristic CSP search algorithms
CSCLP'05 Proceedings of the 2005 Joint ERCIM/CoLogNET international conference on Constraint Solving and Constraint Logic Programming
Hi-index | 0.00 |
For constraint satisfaction problems (CSPs), Haralick & Elliott [1] introduced the Fail-First Principle and defined in it terms of minimizing branch depth. By devising a range of variable ordering heuristics, each in turn trying harder to fail first, Smith & Grant [2] showed that adherence to this strategy does not guarantee reduction in search effort. The present work builds on Smith & Grant. It benefits from the development of a new framework for characterizing heuristic performance that defines two policies, one concerned with enhancing the likelihood of correctly extending a partial solution, the other with minimizing the effort to prove insolubility. The Fail-First Principle can be restated as calling for adherence to the second, fail-first policy, while discounting the other, promise policy. Our work corrects some deficiencies in the work of Smith & Grant, and goes on to confirm their finding that the Fail-First Principle, as originally defined, is insufficient. We then show that adherence to the fail-first policy must be measured in terms of size of insoluble subtrees, not branch depth. We also show that for soluble problems, both policies must be considered in evaluating heuristic performance. Hence, even in its proper form the Fail-First Principle is insufficient. We also show that the “FF” series of heuristics devised by Smith & Grant is a powerful tool for evaluating heuristic performance, including the subtle relations between heuristic features and adherence to a policy.