Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
In search of the best constraint satisfaction search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Backtrack programming techniques
Communications of the ACM
Encyclopedia of Artificial Intelligence
Encyclopedia of Artificial Intelligence
Algorithms and heuristics for constraint satisfaction problems
Algorithms and heuristics for constraint satisfaction problems
Look-ahead value ordering for constraint satisfaction problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A complexity analysis of space-bounded learning algorithms for the constraint satisfaction problem
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Partition search for non-binary constraint satisfaction
Information Sciences: an International Journal
Artificial Intelligence
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The paper focuses on evaluating constraint satisfaction search algorithms on application based random problem instances. The application we use is a well‐studied problem in the electric power industry: optimally scheduling preventive maintenance of power generating units within a power plant. We show how these scheduling problems can be cast as constraint satisfaction problems and used to define the structure of randomly generated non‐binary CSPs. The random problem instances are then used to evaluate several previously studied algorithms. The paper also demonstrates how constraint satisfaction can be used for optimization tasks. To find an optimal maintenance schedule, a series of CSPs are solved with successively tighter cost‐bound constraints. We introduce and experiment with an “iterative learning” algorithm which records additional constraints uncovered during search. The constraints recorded during the solution of one instance with a certain cost‐bound are used again on subsequent instances having tighter cost‐bounds. Our results show that on a class of randomly generated maintenance scheduling problems, iterative learning reduces the time required to find a good schedule.