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Abstract. In recent years, dynamic local search (DLS) clause weighting algorithms have emerged as the local search state-of-the-art for solving propositional satisfiability problems. However, most DLS algorithms require the tuning of domain dependent parameters before their performance becomes competitive. If manual parameter tuning is impractical then various mechanisms have been developed that can automatically adjust a parameter value during the search. To date, the most effective adaptive clause weighting algorithm is RSAPS. However, RSAPS is unable to convincingly outperform the best non-weighting adaptive algorithm AdaptNovelty + , even though manually tuned clause weighting algorithms can routinely outperform the Novelty + heuristic on which AdaptNovelty + is based. In this study we introduce R+DDFW + , an enhanced version of the DDFW clause weighting algorithm developed in 2005, that not only adapts the total amount of weight according to the degree of stagnation in the search, but also incorporates the latest resolution-based preprocessing approach used by the winner of the 2005 SAT competition (R+AdaptNovelty + ). In an empirical study we show R+DDFW + improves on DDFW and outperforms the other leading adaptive (R+Adapt-Novelty + , R+RSAPS) and non-adaptive (R+G 2 WSAT) local search solvers over a range of random and structured benchmark problems.

We present a new and more efficient heuristic by restricting lookahead saturation (LAS) with NVO (neighbourhood variable ordering) and DEW (dynamic equality weighting). We report on the integration of this heuristic in Satz, a high-performance SAT solver, showing empirically that it significantly improves the performance on an extensive range of benchmark problems that exhibit hard structure. 2 Lookahead Saturation with Restriction Lookahead saturation (LAS) based DPLL was studied in [2]. The key idea underlying LAS is to choose a branching variable which is really the best from an irreducible sub-formula at a given node of search tree. LAS is very similar to the "singleton arc consistency" (SAC) algorithm in CSP reasoning [3].

Abstract. We present new results in crossword composition, showing that our program significantly outperforms previous successful techniques in the literature. We emphasize phase transition phenomena, and identify classes of hard problems. Phase transition is shown to occur when varying problem parameters, such as the dictionary size and the number of blocked cells on a grid, of large-size realistic problems.