Connecting Knowledge Compilation Classes and Width Parameters
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Abstract (arXiv)
The field of knowledge compilation establishes the tractability of many tasks by studying how to compile them to Boolean circuit classes obeying some requirements such as structuredness, decomposability, and determinism. However, in other settings such as intensional query evaluation on databases, we obtain Boolean circuits that satisfy some width bounds, e.g., they have bounded treewidth or pathwidth. In this work, we give a systematic picture of many circuit classes considered in knowledge compilation and show how they can be systematically connected to width measures, through upper and lower bounds. Our upper bounds show that bounded-treewidth circuits can be constructively converted to d-SDNNFs, in time linear in the circuit size and singly exponential in the treewidth; and that bounded-pathwidth circuits can similarly be converted to uOBDDs. We show matching lower bounds on the compilation of monotone DNF or CNF formulas to structured targets, assuming a constant bound on the arity (size of clauses) and degree (number of occurrences of each variable): any d-SDNNF (resp., SDNNF) for such a DNF (resp., CNF) must be of exponential size in its treewidth, and the same holds for uOBDDs (resp., n-OBDDs) when considering pathwidth. Unlike most previous work, our bounds apply to any formula of this class, not just a well-chosen family. Hence, we show that pathwidth and treewidth respectively characterize the efficiency of compiling monotone DNFs to uOBDDs and d-SDNNFs with compilation being singly exponential in the corresponding width parameter. We also show that our lower bounds on CNFs extend to unstructured compilation targets, with an exponential lower bound in the treewidth (resp., pathwidth) when compiling monotone CNFs of constant arity and degree to DNNFs (resp., nFBDDs).
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Nov 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 3; 2025: 1; 2026: 2.
9 papers.
- 2021On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Resultscited by 32
- 2024Expected Shapley-Like Scores of Boolean Functions: Complexity and Applications to Probabilistic Databasescited by 6
- 2023Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligencecited by 2
- 2025Compilation and Fast Model Counting beyond CNFcited by 2
- 2024Structured d-DNNF Is Not Closed Under Negationcited by 1
- 2023Bounds on BDD-Based Bucket Eliminationcited by 0
- 2024A Complexity Map of Probabilistic Reasoning for Neurosymbolic Classification Techniquescited by 0
- 2026A canonical generalization of OBDDcited by 0
- 2026Solving Hard XAI Queries Based on a Compiled Dual-Rail Encodingcited by 0
- 2026Solving Hard XAI Queries Based on a Compiled Dual-Rail Encoding4 Sep 2026
- 2026A canonical generalization of OBDD7 Apr 2026
- 2025Compilation and Fast Model Counting beyond CNF1 Feb 2025
- 2024A Complexity Map of Probabilistic Reasoning for Neurosymbolic Classification Techniques12 Apr 2024
- 2024Structured d-DNNF Is Not Closed Under Negation7 Feb 2024
- 2024Expected Shapley-Like Scores of Boolean Functions: Complexity and Applications to Probabilistic Databases12 Jan 2024
- 2023Bounds on BDD-Based Bucket Elimination1 Jun 2023
- 2023Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence6 Mar 2023
- 2021On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results16 Apr 2021