Before a combinatorial problem can be solved by current SAT methods, it must usually be
encoded in conjunctive normal form, which facilitates algorithm implementation and allows a …

Boolean satisfiability (SAT) solvers are at the core of efficient approaches for solving a vast
multitude of practical problems. Moreover, albeit targeting an NP-complete problem, SAT …

One of the most important paradigm shifts in the use of SAT solvers for solving industrial
problems has been the introduction of clause learning. Clause learning entails adding a …

We consider the problem of encoding Boolean cardinality constraints in conjunctive normal
form (CNF). Boolean cardinality constraints are formulae expressing that at most (resp. at …

In this paper, we describe and evaluate three different techniques for translating pseudo-
boolean constraints (linear constraints over boolean variables) into clauses that can be …

In the multi-agent path finding (MAPF) the task is to find non-conflicting paths for multiple
agents. In this paper we present the first SAT-solver for the sum-of-costs variant of MAPF …

In this paper, we propose a canonical prune-and-SAT (CP&SAT) attack for breaking state-of-
the-art routing-based obfuscation techniques. In the CP&SAT attack, we first encode the key …

Abstract Maximum Satisfiability (MaxSAT) is an optimization version of SAT, and many real
world applications can be naturally encoded as such. Solving MaxSAT is an important …

Finite domain propagation solvers effectively represent the possible values of variables by a
set of choices which can be naturally modelled as Boolean variables. In this paper we …

Decision trees of low depth are beneficial for understanding and interpreting the data they
represent. Unfortunately, finding a decision tree of lowest complexity (depth or size) that …