2010/08/25 by Ian P. Gent, Ian Gent, Gent, Ian +6 · 2 citations
Computer Science · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Constraint Satisfaction and Optimization #Data Management and Algorithms #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.1008.4326
arxiv created 2010/08/25 · openalex publication_date 2010/08/25 · arxiv updated 2010/08/26 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.