Artificial Intelligence
Frameworks for abstract interpretation
Acta Informatica
Reasoning about temporal relations: a maximal tractable subclass of Allen's interval algebra
Journal of the ACM (JACM)
Knowledge compilation and theory approximation
Journal of the ACM (JACM)
On the computation of local interchangeability in discrete constraint satisfaction problems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A new approach to cyclic ordering of 2D orientations using ternary relation algebras
Artificial Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Tractable Reasoning in Artificial Intelligence
Tractable Reasoning in Artificial Intelligence
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
Abstracting soft constraints: framework, properties, examples
Artificial Intelligence
An interval-based representation of temporal knowledge
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
Abstraction by interchangeability in resource allocation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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In this paper, we propose to study the interest of relaxing qualitative constraints networks by using the formalism of discrete Constraint Satisfaction Problem (CSP). This allows us to avoid the introduction of new definitions and properties in the domain of qualitative reasoning. We first propose a general (but incomplete) approach to show the unsatisfiability of qualitative networks, by using a relaxation on any set of relations. Interestingly enough, for some qualitative calculi, the proposed scheme can be extended to determine the satisfiability of any qualitative network, leading to an original, simple and complete method. However, as the efficiency of our approach depends on the chosen relaxation, total relations should be preferred due to their connections with the hardness of constraint networks. We then present some preliminary experimental results, with respect to unsatisfiability, which show some promising improvements on some classes of random qualitative networks.