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This book is devoted to recent progress made in solving propositional satisfiability and related problems. Propositional satisfiability is a powerful and general formalism used to solve a wide range of important problems including hardware and software verification. The core of many reasoning problems in automated deduction are propositional. Research into methods to automate such reasoning has therefore a long history in artificial intelligence. In 1957, Allen Newell and Herb Simon introduced the Logic Theory Machine to prove propositional theorems from Whitehead and Russel's "Principia mathematica".
In 1960, Martin Davis and Hillary Putnam introduced their eponymous decision procedure for satisfiability reasoning (though, for space reasons, it was quickly superseded by the modified procedure proposed by Martin Davis, George Logemann and Donald Loveland two years later). In 1971, Stephen Cook's proof that propositional satisfiability is NP-Complete placed satisfiability as the cornerstone of complexity theory.
As this volume demonstrates, research has continued very actively in this area since then. This book follows on from the highly successful volume entitled SAT 2000 published five years ago. The papers in SAT 2005 fall (not entirely neatly) into the following
categories: complete methods, local and stochastic search methods, random problems, applications, and extensions beyond the propositional.
1. Heuristic-Based Backtracking Relaxation for Propositional Satisfiability; Bhalla et al.
2. Symbolic Techniques in Satisfiability Solving; Pan and Vardi.
3. Exponential lower bounds for the running time of DPLL algorithms on satisfiable formulas; Alekhnovich et al.
4. Backdoor Sets for DLL Subsolvers; S. Szeider.
5. The Complexity of Pure Literal Elimination; Johannsen.
6. Clause Weighting Local Search for SAT; Thornton.
7. Solving Non-Boolean Satisfiability Problems with Stochastic Local Search: A Comparison of Encodings; Frisch et al.
8. Regular random k-SAT: properties of balanced formulas; Interian and Selman.
9. Applying SAT Solving for Classification in Finite Algebra; Meier and Sorge.
10. The SAT-Based Approch to Separation Logic; Armando et al.
11. MathSAT: Tight Integration of SAT and Mathematical Decision Procedures; Bozzano et al.