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Learning Nobetter Clauses in Max-SAT Branch and Bound Solvers

机译：在Max-SAT分支和边界求解器中学习Nobetter子句

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摘要

Branch and Bound solvers for Max-SAT are very efficient on random and some crafted instances, as shown in the recent Max-SAT Evaluation results. However, on structured instances (particularly on the ones issued from industrial applications), they are significantly outperformed by other types of Max-SAT solvers. In the SAT context, CDLC solvers perform very well on industrial instances. One of the main reasons of this efficiency is the learning mechanism of nogood clauses, which has been introduced more than fifteen years ago. It allows solvers to learn from their failure with a twofold objective: limit redundancies and lead the exploration to the most promising areas of the search space. We propose in this paper a similar mechanism, which we call nobetter clause learning, adapted to BnB Max-SAT solvers. The results we have obtained show gains on industrial instances. These results call for more work in this direction, to further improve the quality of the information learned and make a better exploitation of them.
机译：如最近的Max-SAT评估结果所示，Max-SAT的分支和边界求解器在随机情况和某些精心制作的实例上非常有效。但是，在结构化实例（尤其是工业应用发布的实例）上，它们的性能明显优于其他类型的Max-SAT求解器。在SAT环境中，CDLC求解器在工业实例上的性能非常好。这种效率高的主要原因之一是nogood子句的学习机制，这种机制已经在15年前被引入。它使求解器可以从失败中吸取教训，其目的有两个：限制冗余，将探索引导到搜索空间中最有希望的领域。在本文中，我们提出了一种类似的机制，称为Nobetter子句学习，适用于BnB Max-SAT求解器。我们获得的结果表明在工业实例方面有所收获。这些结果要求朝这个方向做更多的工作，以进一步提高所学信息的质量并更好地利用它们。

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