Using Branch-and-Bound with Constraint Satisfaction in Optimization Problems

This work integrates three related AI search techniques - constraint satisfaction, branch-and-bound and solution synthesis - and apphes the result to constraint satisfaction problems for which optimal answers are required. This method has already been shown to work well in natural language semantic analysis (Beale, et al, 1996); here we extend the domain to optimizing graph coloring problems, which are abstractions of many common scheduling problems of interest. We demonstrate that the methods used here allow us to determine optimal answers to many types of problems without resorting to heuristic search, and, furthermore can be combined with heuristic search methods for problems with excessive complexity.