Artificial Intelligence - Special issue on knowledge representation
Effective solution of qualitative interval constraint problems
Artificial Intelligence
A survey on temporal reasoning in artificial intelligence
AI Communications
Maintaining knowledge about temporal intervals
Communications of the ACM
Temporal representation and reasoning in artificial intelligence: Issues and approaches
Annals of Mathematics and Artificial Intelligence
A theoretical evaluation of selected backtracking algorithms
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Conditional and composite temporal CSPs
Applied Intelligence
Managing dynamic CSPs with preferences
Applied Intelligence
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There is very little of “time” in a temporal constraint propagation algorithm. Most of these algorithms could easily handle reasoning over any domain mapable onto rational numbers, e.g., weight, frequency, luminosity, etc. Actually some of these algorithms are capable of handling more sophisticated domains than those mapped onto rational numbers, e.g., intervals, or partially ordered objects (say, time). In this article we have generalized such an algorithm, which was originally developed for time-interval domain, to any generic domain, where binary constraints are expressed over arcs of the constraint network. Given the composition table for the primitive relations between a pair of the domain entities (e.g., intervals) as an additional input along with a constraint graph, the algorithm would generate all consistent singleton models for a given network. The algorithm is also extended here to handle uncertainty values.