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The paper evaluates the eectiveness of learning for speeding up the solution of constraint satisfaction problems. It extends previous work (Dechter 1990) by introducing a new and powerful variant of learning and by presenting an extensive empirical study on much larger and more dicult problem instances. Our results show that learning can speed up(More)
We present empirical evidence that the distribution of eeort required to solve CSPs randomly generated at the 50% satissable point, when using a backtracking algorithm, can be approximated by two standard families of continuous probability distribution functions. Solvable problems can be modelled by the Weibull distribution , and unsolvable problems by the(More)
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 h o w these scheduling problems can be cast as constraint(More)
A well-studied problem in the electric power industry is that of optimally scheduling preventative maintenance of power generating units within a power plant. We show how these problems can be cast as constraint satisfaction problems and provide an \iterative learning" algorithm which solves the problem in the following manner. In order to nd an optimal(More)
Haralick and Elliott's full looking ahead algorithm 4 w as presented in the same article as forward checking, but is not as commonly used. We give experimental results which indicate that on some types of constraint satisfaction problems, full looking ahead outperforms forward checking. We also present three new looking ahead algorithms, all variations on(More)