Abstract
Solving constraint optimization problems is hard because it is not enough to find the best solution; an algorithm does not know a candidate is the best solution until it has proven that there are no better solutions. The proof can be long, compared to the time spent to find a good solution. In the cases where there are resource bounds, the proof of optimality may not be achievable and a tradeoff needs to be made between the solution quality and the cost due to the time delay. We propose a decision theoretic meta-reasoning-guided COP solver to address this issue. By choosing the action with the estimated maximal expected utility, the meta-reasoner finds a stopping point with a good tradeoff between the solution quality and the time cost.
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Zheng, J., Horsch, M.C. (2005). A Decision Theoretic Meta-reasoner for Constraint Optimization. In: Kégl, B., Lapalme, G. (eds) Advances in Artificial Intelligence. Canadian AI 2005. Lecture Notes in Computer Science(), vol 3501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424918_8
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DOI: https://doi.org/10.1007/11424918_8
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