Investigating the Grounding Bottleneck for a Large-Scale Configuration Problem: Existing Tools and Constraint-Aware Guessing

2026-01-07

Electronic Proceedings in Theoretical Computer Science

AI Summary

This work addresses the scalability limitations of traditional Answer Set Programming (ASP) solvers when applied to ultra-large-scale configuration problems—such as electronic systems comprising over 30,000 components—where the grounding bottleneck leads to prohibitive memory consumption. To overcome this challenge, the authors propose a constraint-aware guessing strategy integrated with incremental solving, which leverages constraint information to guide variable selection prior to grounding. This approach substantially reduces memory usage while preserving solution correctness. Experimental evaluation on real-world, large-scale configuration instances demonstrates that the method effectively circumvents the memory barriers of existing ASP solvers, significantly enhancing both their scalability and practical applicability in complex industrial settings.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Programming
• Multiagent Systems: Distributed Problem Solving
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• grounding bottleneck
• large-scale configuration
• answer set programming
• memory demands
• scalability

Innovation

• grounding bottleneck
• answer set programming
• constraint-aware guessing
• incremental solving
• large-scale configuration