Summary of Exploiting Symmetries in Mus Computation (extended Version), by Ignace Bleukx et al.
Exploiting Symmetries in MUS Computation (Extended version)
by Ignace Bleukx, Hélène Verhaeghe, Bart Bogaerts, Tias Guns
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper explores the intersection of eXplainable Constraint Solving (XCS) and symmetry detection in unsatisfiable constraint programs. Specifically, it focuses on extracting Minimal Unsatisfiable Subsets (MUSes), which help explain why a constraint specification does not admit a solution. The authors adapt well-known MUS-computation methods to exploit symmetries in the specification, achieving significant reductions in computation time for symmetric problems. This work is important for improving the efficiency of XCS-based systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Symmetry detection is crucial in eXplainable Constraint Solving (XCS) when finding Minimal Unsatisfiable Subsets (MUSes). In traditional satisfaction problems, symmetry handling techniques are well-studied. However, these techniques have not been applied to finding MUSes of unsatisfiable constraint programs. This paper shows how adapting these methods can speed up the computation time for symmetric problems. |
