Exact and approximate reasoning about temporal relations
Computational Intelligence
Effective solution of qualitative interval constraint problems
Artificial Intelligence
Reasoning about qualitative temporal information
Artificial Intelligence - Special volume on constraint-based reasoning
Complexity and algorithms for reasoning about time: a graph-theoretic approach
Journal of the ACM (JACM)
Reasoning about temporal relations: a maximal tractable subclass of Allen's interval algebra
Journal of the ACM (JACM)
Maintaining knowledge about temporal intervals
Communications of the ACM
Synthesizing constraint expressions
Communications of the ACM
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
A Local Search Approach to Modelling and Solving Interval Algebra Problems
Journal of Logic and Computation
The design and experimental analysis of algorithms for temporal reasoning
Journal of Artificial Intelligence Research
SAT-encodings, search space structure, and local search performance
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Solving non-Boolean satisfiability problems with stochastic local search
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Modelling and solving temporal reasoning as propositional satisfiability
Artificial Intelligence
Merging Qualitative Constraints Networks Using Propositional Logic
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Qualitative CSP, finite CSP, and SAT: comparing methods for qualitative constraint-based reasoning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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In this paper, we investigate how an IA network can be effectively encoded into the SAT domain. We propose two basic approaches to modelling an IA network as a CSP: one represents the relations between intervals as variables and the other represents the relations between end-points of intervals as variables. By combining these two approaches with three different SAT encoding schemes, we produced six encoding schemes for converting IA to SAT. These encodings were empirically studied using randomly generated IA problems of sizes ranging from 20 to 100 nodes. A general conclusion we draw from these experimental results is that encoding IA into SAT produces better results than existing approaches. Further, we observe that the phase transition region maps directly from the IA encoding to each SAT encoding, but, surprisingly, the location of the hard region varies according to the encoding scheme. Our results also show a fixed performance ranking order over the various encoding schemes.