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.

翻译：许多工业应用需要找到解决具有挑战性的组合问题的办法。有效消除对称解决方案候选人是高绩效解决方案的关键促进因素之一。然而,现有的基于模型的对称拆解方法仅限于存在一系列具有代表性和容易解决的例子的问题,而实际应用中往往不是这样。这项工作扩大了基于 " 问答组合方案 " 的示范方法的学习框架和实施,以克服这些限制并解决具有挑战性的问题,如 " 伙伴单位问题 " 。特别是,我们将新的冲突分析算法纳入启动逻辑规划系统ILASP,重新定义学习任务,并建议一种新的生成范例的方法来扩大这一方法的规模。为不同类型的伙伴单位进行的实验证明了我们的方法的可适用性,以及由于所了解到的第一阶限制而带来的计算效益。