Automated within-problem learning for Constraint Satisfaction Problems


Within-problem learning, and in particular learning from failure, has proven to be extremely beneficial in solving combinatorial problems in the boolean satisfiability domain (SAT) where both clause learning and clause weighting are used by many of the top SAT solvers. Similar research in solving constraint satisfaction problems (CSPs) has been done on… (More)


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