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 …

Benders decomposition uses a strategy of``learning from one's mistakes.''The aim of this
paper is to extend this strategy to a much larger class of problems. The key is to generalize …

Tree ensembles (TEs) denote a prevalent machine learning model that do not offer
guarantees of interpretability, that represent a challenge from the perspective of explainable …

The problem of enumerating minimal unsatisfiable subsets (MUSes) of an infeasible
constraint system is challenging due first to the complexity of computing even a single MUS …

Abstract Maximum Satisfiability (MaxSAT) is a prototypical constraint optimization problem,
and its generalized version is the (Weighted) Partial MaxSAT problem, denoted as (W) PMS …

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 …

Many industrial optimization problems can be translated to MaxSAT. Although the general
problem is NP hard, like SAT, many practical problems may be solved using modern …

Maximum satisfiability (MaxSAT) is an optimization version of SAT that is solved by finding
an optimal truth assignment instead of just a satisfying one. In MaxSAT the objective function …

Conflict-based Search (CBS) is a effective approach to optimal multi-agent path finding.
However, performance of CBS approaches degrade rapidly in highly-contended graphs with …

Decision lists (DLs) find a wide range of uses for classification problems in Machine
Learning (ML), being implemented in anumber of ML frameworks. DLs are often perceived …