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This book covers recent progress 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. Research into methods to automate such reasoning has therefore a long history in artificial intelligence. This book follows on from the highly successful volume entitled SAT 2000 published five years ago.

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Preface.
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.

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