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CORR
2010
Springer

Using machine learning to make constraint solver implementation decisions

10 years 10 months ago
Using machine learning to make constraint solver implementation decisions
Programs to solve so-called constraint problems are complex pieces of software which require many design decisions to be made more or less arbitrarily by the implementer. These decisions affect the performance of the finished solver significantly [13]. 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 with the alldifferent constraint as an example. Our system is capable of making non-trivial, multi-level decisions that improve over always making a default choice.
Lars Kotthoff, Ian P. Gent, Ian Miguel
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where CORR
Authors Lars Kotthoff, Ian P. Gent, Ian Miguel
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