In AI Planning, as well as Verification, a successful method is to compile the application into boolean satisfiability (SAT), and solve it with state-of-the-art DPLL-based procedures. There is a lack of formal understanding why this works so well. Focussing on the Planning context, we identify a form of problem structure concerned with the symmetrical or asymmetrical nature of the cost of achie...

Preliminaries abstract reduction systems well-founded orderings Propositional logic syntax, semantics calculi: DPLL-procedure, . . . implementation: 2-watched literals, clause learning First-order predicate logic syntax, semantics, model theory, . . . calculi: resolution, tableaux, . . . implementation: sharing, indexing First-order predicate logic with equality term rewriting systems calculi: ...

In the context of DPLL(T), theory propagation is the process of dynamically selecting consequences of a conjunction of constraints from a given set of candidate constraints. We present improvements to a fast theory propagation procedure for difference constraints of the form x− y ≤ c. These improvements are demonstrated experimentally.

Over the years, proof systems for propositional satisfiability (SAT) have been extensively studied. Recently, proof systems for quantified Boolean formulas (QBFs) have also been gaining attention. Q-resolution is a calculus enabling producing proofs from DPLL-based QBF solvers. While DPLL has become a dominating technique for SAT, QBF has been tackled by other complementary and competitive appr...

Zusammenfassung: We study the performance of DPLL algorithms on parameterized problems. In particular, we investigate how difficult it is to decide whether small solutions exist for satisfiability and other combinatorial problems. For this purpose we develop a Prover-Delayer game which models the running time of DPLL procedures and we establish an information-theoretic method to obtain lower bo...

We describe an interpolant-based approach to test generation and model checking for sequential programs. The method generates Floyd/Hoare style annotations of the program on demand, as a result of failure to achieve goals, in a manner analogous to conflict clause learning in a DPLL style SAT solver.

Q-resolution and its variations provide the underlying proof systems for the DPLL-based QBF solvers. While (long-distance) Q-resolution models a conflict driven clause learning (CDCL) QBF solver, it is not known whether the inverse is also true. This paper provides a negative answer to this question. This contrasts with SAT solving, where CDCL solvers have been shown to simulate resolution.

This proposal outlines the creation of an adaptive algorithm for the Propositional Satisfiability Problem (SAT). The SAT problem is of great importance in theoretical computer science and verification. The proposed algorithm, or solver, uses the well-known DPLL approach coupled with a variety of optimizations and extensions which are selectively turned on and off during runtime to adapt to the ...

The search of a precise measure of what hardness of SAT instances means for state-of-the-art solvers is a relevant research question. Among others, the space complexity of tree-like resolution (also called hardness), the minimal size of strong backdoors and of cycle-cutsets, and the treewidth can be used for this purpose. We propose the use of the tree-like space complexity as a solid candidate...

DPLLs are used widely in communications systems like radio, telecommunications, computer and other electronic applications. Digital PLLs are a type of PLL used to synchronize digital signals. While DPLLs input and outputs are typically all digital, they do have internal functions which are dependent on analog signals. The main blocks in demodulator are DDS core, Filters, Arc tan estimator, Loop...