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

The rapid rise of Artificial Intelligence (AI) and Machine Learning (ML) has invoked the need
for explainable AI (XAI). One of the most prominent approaches to XAI is to train rule-based …

Many problems in real life can be transformed into combinatorial optimization problems. As
an important heuristic, the local search algorithm has achieved outstanding performance in …

Constraint satisfaction problems have a wide range of applications in areas such as basic
computer theory research and artificial intelligence, and many major studies in industry are …

Local search is an effective method for solving large-scale combinatorial optimization
problems, and it has made remarkable progress in recent years through several subtle …

Significant advances have been recently made in the development of increasingly effective
in-exact (or incomplete) search algorithms—particularly geared towards finding good though …

Uniform random satisfiability (URS) and hard random satisfiability (HRS) are two significant
generalizations of random satisfiability (RS). Recently, great breakthroughs have been …

The maximum bisection problem (max-bisection) belongs to a family of well-known graph
partitioning problems with wide applications. In this study, we develop a simple and efficient …

At the heart of computer science and artificial intelligence, logic is often used as a powerful
language to model and solve complex problems that arise in academia and in real-world …

As a possible countermeasure to the lack in algorithms for solving maximum regularization
satisfiability problems as well as inadequate accuracy of intelligent optimization algorithms …