Arboricity and subgraph listing algorithms
SIAM Journal on Computing
Arc and path consistence revisited
Artificial Intelligence
Comments on Mohr and Henderson's path consistency algorithm
Artificial Intelligence
Processing disjunctions in temporal constraint networks
Artificial Intelligence
Path-consistency: when space misses time
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Reformulation of Temporal Constraint Networks
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
The Knowledge Engineering Review
Spatio-temporal event stream processing in multimedia communication systems
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Journal of Artificial Intelligence Research
Distributed algorithms for solving the multiagent temporal decoupling problem
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Connecting the dots: constructing spatiotemporal episodes from events schemas
Transactions on Computational Science VI
Distributed reasoning for multiagent simple temporal problems
Journal of Artificial Intelligence Research
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Among the local consistency techniques used in the resolution of constraint satisfaction problems (CSPs), path consistency (PC) has received a great deal of attention. A constraint graph G is PC if for any valuation of a pair of variables that satisfy the constraint in G between them, one can find values for the intermediate variables on any other path in G between those variables so that all the constraints along that path are satisfied. On complete graphs, Montanari showed that PC holds if and only if each path of length two is PC. By convention, it is therefore said that a CSP is PC if the completion of its constraint graph is PC. In this paper, we show that Montanari's theorem extends to triangulated graphs. One can therefore enforce PC on sparse graphs by triangulating instead of completing them. The advantage is that with triangulation much less universal constraints need to be added. We then compare the pruning capacity of the two approaches. We show that when the constraints are convex, the pruning capacity of PC on triangulated graphs and their completion are identical on the common edges. Furthermore, our experiments show that there is little difference for general nonconvex problems.