Phase Transition Behavior in Knowledge Compilation

The study of phase transition behaviour in SAT has led to deeper understanding and algorithmic improvements of 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 CSP community on phase transition behavior in SAT/CSP. In a similar spirit, 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. Furthermore, we summarize our empirical study in terms of two concrete conjectures; a rigorous study of these conjectures will possibly require new theoretical tools.

[1]  Adnan Darwiche,et al.  Using DPLL for Efficient OBDD Construction , 2004, SAT.

[2]  Hector J. Levesque,et al.  Hard and Easy Distributions of SAT Problems , 1992, AAAI.

[3]  Allan Sly,et al.  Proof of the Satisfiability Conjecture for Large k , 2014, STOC.

[4]  Dimitris Achlioptas,et al.  Random Satisfiability , 2009, Handbook of Satisfiability.

[5]  Randal E. Bryant,et al.  Graph-Based Algorithms for Boolean Function Manipulation , 1986, IEEE Transactions on Computers.

[6]  Tad Hogg,et al.  Exploiting the Deep Structure of Constraint Problems , 1994, Artif. Intell..

[7]  Moshe Y. Vardi,et al.  Random 3-SAT and BDDs: The Plot Thickens Further , 2001, CP.

[8]  Christian J. Muise,et al.  Dsharp: Fast d-DNNF Compilation with sharpSAT , 2012, Canadian Conference on AI.

[9]  Riccardo Zecchina,et al.  Survey propagation: An algorithm for satisfiability , 2002, Random Struct. Algorithms.

[10]  Moshe Y. Vardi,et al.  Combining the k-CNF and XOR Phase-Transitions , 2016, IJCAI.

[11]  Moshe Y. Vardi,et al.  The Hard Problems Are Almost Everywhere For Random CNF-XOR Formulas , 2017, IJCAI.

[12]  Yannis C. Stamatiou,et al.  Random Constraint Satisfaction a More Accurate Picture , 2022 .

[13]  B. Bollobás The evolution of random graphs , 1984 .

[14]  Toby Walsh,et al.  Random Constraint Satisfaction: Flaws and Structure , 2004, Constraints.

[15]  Eliezer L. Lozinskii,et al.  The Good Old Davis-Putnam Procedure Helps Counting Models , 2011, J. Artif. Intell. Res..

[16]  Wei Li,et al.  The SAT phase transition , 1999, ArXiv.

[17]  Amin Coja-Oghlan,et al.  Algorithmic Barriers from Phase Transitions , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.

[18]  Simone Bova SDDs Are Exponentially More Succinct than OBDDs , 2016, AAAI.

[19]  Toby Walsh,et al.  Beyond NP: the QSAT phase transition , 1999, AAAI/IAAI.

[20]  Minghao Yin,et al.  Phase Transitions in Knowledge Compilation: An Experimental Study , 2011, SAT.

[21]  Umut Oztok,et al.  An Exhaustive DPLL Algorithm for Model Counting , 2018, J. Artif. Intell. Res..

[22]  Adnan Darwiche,et al.  New Advances in Compiling CNF into Decomposable Negation Normal Form , 2004, ECAI.

[23]  Pierre Marquis,et al.  A Knowledge Compilation Map , 2002, J. Artif. Intell. Res..

[24]  Peter C. Cheeseman,et al.  Where the Really Hard Problems Are , 1991, IJCAI.

[25]  Adnan Darwiche,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence SDD: A New Canonical Representation of Propositional Knowledge Bases , 2022 .

[26]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[27]  Adnan Darwiche,et al.  On the Tractable Counting of Theory Models and its Application to Truth Maintenance and Belief Revision , 2001, J. Appl. Non Class. Logics.

[28]  Jean-Marie Lagniez,et al.  An Improved Decision-DNNF Compiler , 2017, IJCAI.

[29]  Umut Oztok,et al.  A Top-Down Compiler for Sentential Decision Diagrams , 2015, IJCAI.

[30]  Kuldeep S. Meel,et al.  Phase Transition Behavior of Cardinality and XOR Constraints , 2019, IJCAI.

[31]  Roberto J. Bayardo,et al.  Counting Models Using Connected Components , 2000, AAAI/IAAI.

[32]  Lenka Zdeborová,et al.  Random Subcubes as a Toy Model for Constraint Satisfaction Problems , 2007, ArXiv.