Phase Transition Behavior in Knowledge Compilation

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Abstract

The study of phase transition behaviour in SAT has led to deeper understanding and algorithmic improvements in modern SAT solvers. Motivated by these prior studies of phase transitions in SAT, we seek to study the behaviour of size and compile-time behaviour for random k-CNF formulas in the context of knowledge compilation. We perform a rigorous empirical study and analysis of the size and runtime behavior for different knowledge compilation forms (and their corresponding compilation algorithms): d-DNNFs, SDDs and OBDDs across multiple tools and compilation algorithms. We employ instances generated from the random k-CNF model with varying generation parameters to empirically reason about the expected and median behavior of size and compilation-time for these languages. Our work is similar in spirit to the early work in the CSP community on phase transition behavior in SAT/CSP. We identify the interesting behavior with respect to different parameters: clause density and solution density, a novel control parameter that we identify for the study of phase transition behavior in the context of knowledge compilation. We summarize our empirical study in terms of two concrete conjectures; a rigorous study of these conjectures will possibly require new theoretical tools.

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Gupta, R., Roy, S., & Meel, K. S. (2020). Phase Transition Behavior in Knowledge Compilation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12333 LNCS, pp. 358–374). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58475-7_21

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