Answer set programming (ASP) is a paradigm for modeling knowledge-intensive domains and solving challenging reasoning problems. In ASP solving, a typical strategy is to preprocess problem instances by rewriting complex rules into simpler ones. Normalization is a rewriting process that removes extended rule types altogether in favor of normal rules. Recently, such techniques led to optimization rewriting in ASP, where the goal is to boost answer set optimization by refactoring the optimization criteria of interest. In this paper, we present a novel, general, and effective technique for optimization rewriting based on comparator networks which are specific kinds of circuits for reordering the elements of vectors. The idea is to connect an ASP encoding of a comparator network to the literals being optimized and to redistribute the weights of these literals over the structure of the network. The encoding captures information about the weight of an answer set in auxiliary atoms in a structured way that is proven to yield exponential improvements during branch-and-bound optimization on an infinite family of example programs. The used comparator network can be tuned freely, for example, to find the best size for a given benchmark class. Experiments show accelerated optimization performance on several benchmark problems.

中文翻译：

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使用加权比较器网络促进答案集优化

答案集编程 (ASP) 是为知识密集型领域建模和解决具有挑战性的推理问题的范例。在 ASP 求解中，一个典型的策略是通过将复杂规则重写为更简单的规则来预处理问题实例。规范化是一个重写过程，它完全删除扩展规则类型以支持正常规则。最近，此类技术导致 ASP 中的优化重写，其目标是通过重构感兴趣的优化标准来促进答案集优化。在本文中，我们提出了一种新颖、通用且有效的基于比较器网络的优化重写技术，比较器网络是用于重新排序向量元素的特定类型的电路。这个想法是将比较器网络的 ASP 编码连接到正在优化的文字，并在网络结构上重新分配这些文字的权重。编码以结构化方式捕获有关辅助原子中答案集权重的信息，经证明在无限系列示例程序的分支定界优化期间产生指数级改进。使用的比较器网络可以自由调整，例如，为给定的基准类找到最佳大小。实验显示了在几个基准问题上的加速优化性能。编码以结构化方式捕获有关辅助原子中答案集权重的信息，经证明在无限系列示例程序的分支定界优化期间产生指数级改进。使用的比较器网络可以自由调整，例如，为给定的基准类找到最佳大小。实验显示了在几个基准问题上的加速优化性能。编码以结构化方式捕获有关辅助原子中答案集权重的信息，经证明在无限系列示例程序的分支定界优化期间产生指数级改进。使用的比较器网络可以自由调整，例如，为给定的基准类找到最佳大小。实验显示了在几个基准问题上的加速优化性能。