In this paper, tabu search for SAT is investigated from an experimental point of view. To this end, TSAT, a basic tabu search algorithm for SAT, is introduced and compared with Selman et al. Random Walk Strategy GSAT procedure, in short RWS-GSAT. TSAT does not involve the additional

We report on the performance of an enhanced version of the "Davis-Putnam" (DP) proof procedure for propositional satisfiability (SAT) on large instances derived from realworld problems in planning, scheduling, and circuit diagnosis and synthesis. Our results show that incorporating CSP lookback techniques -- especially the relatively new technique of relevance-bounded learning -- renders easy many problems which otherwise are beyond DP's reach. Frequently they make DP, a systematic algorithm, perform as well or better than stochastic SAT algorithms such as GSAT or WSAT. We recommend that such techniques be included as options in implementations of DP, just as they are in systematic algorithms for the more general constraint satisfaction problem. Introduction While CNF propositional satisfiability (SAT) is a specific kind constraint satisfaction problem (CSP), until recently there has been little application of popular CSP look-back techniques in SAT algorithms. In previo...

Abstract. While CNF propositional satisfiability (SAT) is a sub-class of the more general constraint satisfaction problem (CSP), conventional wisdom has it that some well-known CSP look-back techniques-- including backjumping and learning-- are of little use for SAT. We enhance the Tableau SAT algorithm of Crawford and Auton with look-back techniques and evaluate its performance on problems specifically designed to challenge it. The Random 3-SAT problem space has commonly been used to benchmark SAT algorithms because consistently difficult instances can be found near a region known as the phase transition. We modify Random 3-SAT in two ways which make instances even harder. First, we evaluate problems with structural regularities and find that CSP look-back techniques offer little advantage. Second, we evaluate problems in which a hard unsatisfiable instance of medium size is embedded in a larger instance, and we find the look-back enhancements to be indispensable. Without them, most instances are “exceptionally hard ”-orders of magnitude harder than typical Random 3-SAT instances with the same surface characteristics.

ion and the CSP Phase Transition Boundary Robert Schrag and Daniel Miranker Department of Computer Sciences and The Applied Research Laboratories Taylor Hall 2.124, Mail Code C0500 University of Texas at Austin Austin, TX USA 78712 Internet: fschrag---mirankerg@cs.utexas.edu WWW: http://www.cs.utexas.edu/users/schrag September 14, 1995 Abstract We describe an abstraction technique for CSPs and characterize its effectiveness with reference to a parameterized space of random CSPs. We find that effectiveness drops off suddenly as constraints are loosened, their number is increased, or the degree of abstraction is increased, and the drop-off is related intimately to the problem space's underlying satisfiability phase transition. The abstraction tends to loosen constraints, resulting in a shifted phase transition boundary for the problem space. Qualitatively, the boundary restricts effectiveness to input CSPs with very tight constraints. Quantitatively, we show that in a specific regio...