解决大规模配置问题的内存瓶颈，提出约束感知猜测新方法。
Investigating the Grounding Bottleneck for a Large-Scale Configuration Problem: Existing Tools and Constraint-Aware Guessing
- 提出约束感知猜测法，缓解逻辑规划中的接地瓶颈
- 在超3万组件系统中，显著降低内存占用
- 适合需要高效求解大规模配置问题的研究者
答案集编程（ASP）旨在实现人工智能愿景：用户定义问题，计算机自动求解。尽管已在多个领域成功应用，但当前ASP求解技术能否应对大规模配置问题仍存疑。以电子系统配置为例，此类问题可能包含超过30,000个组件。本文研究现有ASP技术在该类问题上的潜力与局限，聚焦于“接地瓶颈”——即问题规模扩大时内存需求急剧上升的问题。实验表明，增量求解虽有效，但仍受限于内存。基于对接地过程的分析，提出约束感知猜测方法，大幅降低内存消耗。
原文摘要 · Abstract (English)
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.
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