A Strong Local Consistency for Constraint Satisfaction
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Domain filtering consistencies
Journal of Artificial Intelligence Research
A simple way to improve path consistency processing in interval algebra networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Avian influenza: Temporal modeling of a human to human transmission case
Expert Systems with Applications: An International Journal
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One of the main factors limiting the use of path consistency algorithms in real life applications is their high space complexity. Han and Lee [AI Journal, p. 125-130, 1988] presented a path consistency algorithm, PC-4, with O(n^3a^3) space complexity, which makes it practicable only for small problems. I present a new path consistency algorithm, PC-5, which has an O(n^3a^2) space complexity while retaining the worst-case time complexity of PC-4. Moreover, the new algorithm exhibits a much better average-case time complexity. The new algorithm is based on the idea (due to Bessiere [AI Journal, p. 179-190, 1994]) that, at any time, only a minimal amount of support has to be found and recorded for a labeling to establish its viability; one has to look for a new support only if the current support is eliminated. I also show that PC-5 can be improved further to yield an algorithm, PC5++, with even better average-case performance and the same space complexity.