Pruning Boolean d-DNNF Circuits Through Tseitin-Awareness
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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.
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References · 6
- 2017A Semantic Loss Function for Deep Learning with Symbolic Knowledgecited by 267
- 2019Smoothing Structured Decomposable Circuitscited by 14
- 2023Scaling Integer Arithmetic in Probabilistic Programscited by 6
- 2024Semirings for Probabilistic and Neuro-Symbolic Logic Programmingcited by 6
- 2017On Compiling DNNFs without Determinismcited by 4
- 2023SharpSAT-TD in Model Counting Competitions 2021-2023cited by 3
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