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Parameterized Complexity Of Representing Models Of MSO Formulas

arXiv AI Archived Apr 13, 2026 ✓ Full text saved

arXiv:2604.08707v1 Announce Type: new Abstract: Monadic second order logic (MSO2) plays an important role in parameterized complexity due to the Courcelle's theorem. This theorem states that the problem of checking if a given graph has a property specified by a given MSO2 formula can be solved by a parameterized linear time algorithm with respect to the treewidth of the graph and the size of the formula. We extend this result by showing that models of MSO2 formula with free variables can be repr

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    Computer Science > Artificial Intelligence [Submitted on 9 Apr 2026] Parameterized Complexity Of Representing Models Of MSO Formulas Petr Kučera, Petr Martinek Monadic second order logic (MSO2) plays an important role in parameterized complexity due to the Courcelle's theorem. This theorem states that the problem of checking if a given graph has a property specified by a given MSO2 formula can be solved by a parameterized linear time algorithm with respect to the treewidth of the graph and the size of the formula. We extend this result by showing that models of MSO2 formula with free variables can be represented with a decision diagram whose size is parameterized linear in the above mentioned parameter. In particular, we show a parameterized linear upper bound on the size of a sentential decision diagram (SDD) when treewidth is considered and a parameterized linear upper bound on the size of an ordered binary decision diagram (OBDD) when considering the pathwidth in the parameter. In addition, building on a lower bound on the size of OBDD by Razgon (2014), we show that there is an MSO2 formula and a class of graphs with bounded treewidth which do not admit an OBDD with the size parameterized by the treewidth. Our result offers a new perspective on the Courcelle's theorem and connects it to the area of knowledge representation. Subjects: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC) Cite as: arXiv:2604.08707 [cs.AI]   (or arXiv:2604.08707v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.08707 Focus to learn more Submission history From: Petr Kučera [view email] [v1] Thu, 9 Apr 2026 18:56:50 UTC (126 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CC References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    Apr 13, 2026
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    Apr 13, 2026
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