Answer set programming (ASP) aims to realize the AI vision: The user specifies the problem, and the computer solves it. Indeed, ASP has made this vision true in many application domains. However, will current ASP solving techniques scale up for large configuration problems? As a benchmark for such problems, we investigated the configuration of electronic systems, which may comprise more than 30,000 components. We show the potential and limits of current ASP technology, focusing on methods that address the so-called grounding bottleneck, i.e., the sharp increase of memory demands in the size of the problem instances. To push the limits, we investigated the incremental solving approach, which proved effective in practice. However, even in the incremental approach, memory demands impose significant limits. Based on an analysis of grounding, we developed the method constraint-aware guessing, which significantly reduced the memory need.

翻译：答案集编程（ASP）旨在实现人工智能的愿景：用户指定问题，计算机解决问题。事实上，ASP已在众多应用领域实现了这一愿景。然而，当前ASP求解技术能否扩展至大规模配置问题？作为此类问题的基准，我们研究了电子系统的配置问题，其可能包含超过30,000个组件。我们展示了当前ASP技术的潜力与局限，重点关注解决所谓“基础瓶颈”（即问题实例规模导致内存需求急剧增加）的方法。为突破现有局限，我们研究了增量求解方法，该方法在实践中被证明是有效的。然而，即使在增量方法中，内存需求仍构成显著限制。基于对基础过程的分析，我们开发了约束感知推测方法，该方法显著降低了内存需求。