A decision theoretic meta-reasoner for constraint optimization Lecture Notes in Computer Science 3501 53?65 Michael L Anderson michael anderson@fandm edu is an Assistant Professor in the Department of...


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    • Author:  by Jingfang Zheng  Michael C. Horsch  

    • Abstract:  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-guide d 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. 1.

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