复杂组合问题对称破缺约束的有效提升
Efficient lifting of symmetry breaking constraints for complex combinatorial problems
摘要：
许多工业应用需要找到具有挑战性的组合问题的解决方案。有效地消除对称解决方案候选是高性能求解的关键推动因素之一。然而，现有的基于模型的对称性破缺方法仅限于一组具有代表性且易于求解的实例，而实际应用中往往不是这种情况。这项工作扩展了学习框架和实施基于模型的方法，答案集编程，以克服这些限制，并解决具有挑战性的问题，如合作伙伴单位的问题。特别是，我们将一个新的冲突分析算法在归纳逻辑程序设计系统ILASP，重新定义的学习任务，并提出了一个新的例子生成方法，以扩大的方法。针对不同类型的合作伙伴单位问题实例进行的实验证明了我们的方法的适用性和由于学习到的一阶约束而带来的计算效益。
英文摘要：
Many industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, existing model-based approaches for symmetry breaking are limited to problems for which a set of representative and easily-solvable instances is available, which is often not the case in practical applications. This work extends the learning framework and implementation of a model-based approach for Answer Set Programming to overcome these limitations and address challenging problems, such as the Partner Units Problem. In particular, we incorporate a new conflict analysis algorithm in the Inductive Logic Programming system ILASP, redefine the learning task, and suggest a new example generation method to scale up the approach. The experiments conducted for different kinds of Partner Units Problem instances demonstrate the applicability of our approach and the computational benefits due to the first-order constraints learned.
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