Daniel Frost

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The paper evaluates the e ectiveness 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 di cult 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)
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)
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(More)
Over the past twenty years a number of backtracking algorithms for constraint satisfaction problems have been developed. This survey describes the basic backtrack search within the search space framework and then presents a number of improvements including look-back methods such as backjumping, constraint recording, backmarking, and look-ahead methods such(More)