Abstract. We present a novel preprocessing technique to automatically reduce the size of Boolean formulas. This technique, called Bounded Variable Addition (BVA), exchanges clauses for variables. Similar to other preprocessing techniques, BVA greedily lowers the sum of variables and clauses, a rough measure for the hardness to solve a formula. We show that cardinality constraints (CCs) can efficiently be reencoded: from a naive CC encoding, BVA automatically generates a compact encoding, which is smaller than sophisticated encodings. Experimental results show that applying BVA can improve SAT solving performance.

Abstract. Quantitative information flow analysis (QIF) is a portfolio of security techniques quantifying the flow of confidential information to public ports. In this paper, we advance the state of the art in QIF for imperative programs. We present both an abstract formulation of the analysis in terms of verification condition generation, logical projection and model counting, and an efficient concrete implementation targeting ANSI C programs. The implementation combines various novel and existing SAT-based tools for bounded model checking, # SAT solving in presence of projection, and SAT preprocessing. We evaluate the technique on synthetic and semi-realistic benchmarks.

Abstract. We present novel approaches to detect cardinality constraints expressed in CNF. The first approach is based on a syntactic analysis of specific data structures used in SAT solvers to represent binary and ternary clauses, whereas the second approach is based on a semantic analysis by unit propagation. The syntactic approach computes an approximation of the cardinality constraints AtMost-1 and AtMost-2 constraints very fast, whereas the semantic approach has the property to be generic, i.e. it can detect cardinality constraints AtMost-k for any k, at a higher computation cost. Our experimental results suggest that both approaches are efficient at recovering AtMost-1 and AtMost-2 cardinality constraints.

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The design of efficient solutions for abstract argumentation problems is a crucial step towards advanced argumentation systems. One of the most prominent approaches in the literature is to use Answer-Set Programming (ASP) for this endeavor. In this paper, we present new encodings for three prominent argumentation semantics using the concept of conditional literals in disjunctions as provided by the ASP-system clingo. Our new encodings are not only more succinct than previous versions, but also outperform them on standard benchmarks.