Enhancing Davis Putnam with extended binary clause reasoning

The backtracking based Davis Putnam (DPLL) procedure remains the dominant method for deciding the satisfiability of a CNF formula. In recent years there has been much work on improving the basic procedure by adding features like improved heuristics and data structures, intelligent backtracking, clause learning, etc. Reasoning with binary clauses in DPLL has been a much discussed possibility for achieving improved performance, but to date solvers based on this idea have not been competitive with the best unit propagation based DPLL solvers. In this paper we experiment with a DPLL solver called 2CLS+EQ that makes more extensive use of binary clause reasoning than has been tried before. The results are very encouraging-2CLS+EQ is competitive with the very best DPLL solvers. The techniques it uses also open up a number of other possibilities for increasing our ability to solve SAT problems.

[1]  Armando Tacchella,et al.  Benefits of Bounded Model Checking at an Industrial Setting , 2001, CAV.

[2]  Fahiem Bacchus,et al.  Extending Forward Checking , 2000, CP.

[3]  Chu Min Li,et al.  Heuristics Based on Unit Propagation for Satisfiability Problems , 1997, IJCAI.

[4]  Michael L. Littman,et al.  MAXPLAN: A New Approach to Probabilistic Planning , 1998, AIPS.

[5]  Chu Min Li,et al.  Integrating Equivalency Reasoning into Davis-Putnam Procedure , 2000, AAAI/IAAI.

[6]  David G. Mitchell,et al.  Finding hard instances of the satisfiability problem: A survey , 1996, Satisfiability Problem: Theory and Applications.

[7]  Roberto J. Bayardo,et al.  Using CSP Look-Back Techniques to Solve Real-World SAT Instances , 1997, AAAI/IAAI.

[8]  J. Freeman Improvements to propositional satisfiability search algorithms , 1995 .

[9]  Ronen I. Brafman,et al.  A simplifier for propositional formulas with many binary clauses , 2001, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Rina Dechter,et al.  Resolution versus Search: Two Strategies for SAT , 2000, Journal of Automated Reasoning.

[11]  Bart Selman,et al.  Planning as Satisfiability , 1992, ECAI.

[12]  Allen Van Gelder,et al.  Combining Preorder and Postorder Resolution in a Satisfiability Solver , 2001, Electron. Notes Discret. Math..

[13]  Hantao Zhang,et al.  SATO: An Efficient Propositional Prover , 1997, CADE.

[14]  Philippe Chatalic,et al.  SatEx: A Web-based Framework for SAT Experimentation , 2001, Electron. Notes Discret. Math..

[15]  Masahiro Fujita,et al.  Symbolic model checking using SAT procedures instead of BDDs , 1999, DAC '99.