mlsanityData to 7 Oct 2026

Pruning Boolean d-DNNF Circuits Through Tseitin-Awareness

arXiv 2407.17951 ↗First posted 25 Jul 2024cs.AI cs.LOcited by 0

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Abstract (arXiv)

Boolean circuits in d-DNNF form enable tractable probabilistic inference. However, as a key insight of this work, we show that commonly used d-DNNF compilation approaches introduce irrelevant subcircuits. We call these subcircuits Tseitin artifacts, as they are introduced due to the Tseitin transformation step -- a well-established procedure to transform any circuit into the CNF format required by several d-DNNF knowledge compilers. We discuss how to detect and remove both Tseitin variables and Tseitin artifacts, leading to more succinct circuits. We empirically observe an average size reduction of 77.5% when removing both Tseitin variables and artifacts. The additional pruning of Tseitin artifacts reduces the size by 22.2% on average. This significantly improves downstream tasks that benefit from a more succinct circuit, e.g., probabilistic inference tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 33 references that are in the map. First posted on arXiv 25 Jul 2024.

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References · 6

  1. 2017A Semantic Loss Function for Deep Learning with Symbolic Knowledgecited by 267
  2. 2019Smoothing Structured Decomposable Circuitscited by 14
  3. 2023Scaling Integer Arithmetic in Probabilistic Programscited by 6
  4. 2024Semirings for Probabilistic and Neuro-Symbolic Logic Programmingcited by 6
  5. 2017On Compiling DNNFs without Determinismcited by 4
  6. 2023SharpSAT-TD in Model Counting Competitions 2021-2023cited by 3

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