Answer-set programming (ASP) is a prominent approach to declarative problem solving that is increasingly used to tackle challenging optimisation problems. We present an approach to leverage ASP optimisation by using large-neighbourhood search (LNS), which is a meta-heuristic where parts of a solution are iteratively destroyed and reconstructed in an attempt to improve an overall objective. In our LNS framework, neighbourhoods can be specified either declaratively as part of the ASP encoding or automatically generated by code. Furthermore, our framework is self-adaptive, i.e., it also incorporates portfolios for the LNS operators along with selection strategies to adjust search parameters on the fly. The implementation of our framework, the system ALASPO, currently supports the ASP solver clingo, as well as its extensions clingo-dl and clingcon that allow for difference and full integer constraints, respectively. It utilises multi-shot solving to efficiently realise the LNS loop and in this way avoids program regrounding. We describe our LNS framework for ASP as well as its implementation, discuss methodological aspects, and demonstrate the effectiveness of the adaptive LNS approach for ASP on different optimisation benchmarks, some of which are notoriously difficult, as well as real-world applications for shift planning, configuration of railway-safety systems, parallel machine scheduling, and test laboratory scheduling.

中文翻译：

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用于优化答案集规划的自适应大邻域搜索


答案集编程 （ASP） 是一种重要的声明式问题解决方法，越来越多地用于解决具有挑战性的优化问题。我们提出了一种通过使用大邻域搜索 （LNS） 来利用 ASP 优化的方法，这是一种元启发式方法，其中解决方案的某些部分被迭代销毁和重建，以试图改进整体目标。在我们的 LNS 框架中，邻域可以作为 ASP 编码的一部分以声明方式指定，也可以由代码自动生成。此外，我们的框架是自适应的，即它还结合了 LNS 运算符的投资组合以及选择策略，以动态调整搜索参数。我们的框架 ALASPO 系统的实现目前支持 ASP 求解器 clingo，以及其扩展 clingo-dl 和 clingcon，它们分别允许差分和全整数约束。它利用多镜头求解来有效地实现 LNS 回路，从而避免程序重新接地。我们描述了我们的 ASP LNS 框架及其实施，讨论了方法方面，并展示了自适应 LNS 方法在 ASP 的不同优化基准上的有效性，其中一些是众所周知的困难，以及轮班规划、铁路安全系统配置、并行机器调度和测试实验室调度的实际应用。