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

TL;DR

This paper examines the scalability challenges of answer set programming (ASP) in large electronic configuration problems, analyzing grounding bottlenecks and proposing a constraint-aware guessing method to reduce memory usage.

Contribution

It introduces a novel constraint-aware guessing technique that mitigates grounding bottlenecks in ASP, enabling more scalable solutions for large configuration problems.

Findings

Incremental solving improves scalability but still faces memory limits.

Constraint-aware guessing significantly reduces memory demands.

Current ASP methods have potential but are limited by grounding issues.

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.