2010/05/19 by Lars Kotthoff, Kotthoff, Lars, Ian P. Gent +4
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Constraint Satisfaction and Optimization #Data Management and Algorithms #FOS: Computer and information sciences #Software Engineering Research #cs.AI
paper · pdf · doi:10.48550/arxiv.1005.3502
arxiv created 2010/05/19 · openalex publication_date 2010/05/19 · arxiv updated 2010/05/20 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
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. 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.