Machine learning for constraint solver design -- A case study for the alldifferent constraint

Details Machine learning for constraint solver design -- A case study for the alldifferent constraint Machine learning for constraint solver design -- A case study for the alldifferent constraint

2010-08-25

arxiv.org/abs/1008.4326v1

Computer Science

Artificial Intelligence

Scientific paper

Constraint solvers are complex pieces of software which require many design decisions to be made by the implementer based on limited information. These decisions affect the performance of the finished solver significantly. Once a design decision has been made, it cannot easily be reversed, although a different decision may be more appropriate for a particular problem. We investigate using machine learning to make these decisions automatically depending on the problem to solve. We use the alldifferent constraint as a case study. Our system is capable of making non-trivial, multi-level decisions that improve over always making a default choice and can be implemented as part of a general-purpose constraint solver.

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