In recent years, Satisfiability Modulo Theory (SMT) solvers are becoming increasingly popular in the Computer Aided Verification and Reasoning community. Used natively or as back-engines, they are accumulating a record of success stories and, as witn

Answer-set programming (ASP) is a truly declarative programming paradigm proposed in the area of non-monotonic reasoning and logic programming, which has been recently employed in many applications. The development of efficient ASP systems is, thus, crucial. Having in mind the task of improving the solving methods for ASP, there are two usual ways to reach this goal: (i) extending state-of-the-art techniques and ASP solvers or (ii) designing a new ASP solver from scratch. An alternative to these trends is to build on top of state-of-the-art solvers, and to apply machine learning techniques for choosing automatically the “best” available solver on a per-instance basis.In this paper, we pursue this latter direction. We first define a set of cheap-to-compute syntactic features that characterize several aspects of ASP programs. Then, we apply classification methods that, given the features of the instances in atrainingset and the solvers' performance on these instances, inductively learn algorithm selection strategies to be applied to atestset. We report the results of a number of experiments considering solvers and different training and test sets of instances taken from the ones submitted to the “System Track” of the Third ASP Competition. Our analysis shows that by applying machine learning techniques to ASP solving, it is possible to obtain very robust performance: our approach can solve more instances compared with any solver that entered the Third ASP Competition.

Industry 4.0 has driven a paradigm shift in manufacturing, pushing industries to adopt innovative technologies for more efficient decision-making. A key component of this revolution is predictive maintenance, which plays a central role in this transformation by leveraging, among others methods, supervised machine learning techniques to anticipate equipment failures, optimise maintenance schedules, and enhance operational efficiency. This study presents a Systematic Literature Review (SLR) of 216 peer-reviewed papers published between 2019 and 2024, analysing the adoption of supervised machine learning techniques for predictive maintenance in various industrial domains, including manufacturing, machinery, energy, and smart systems. Unlike previous SLRs that broadly examine ML applications, this review provides a structured taxonomy of predictive maintenance methods, highlighting their domain-specific usage and prevalence in safety-critical industries. Additionally, we analyse the types of datasets used, revealing a strong preference for real-world data but limited public availability, which poses challenges for reproducibility and benchmarking. This study identifies key trends in ML adoption and offers insights into future research directions, thereby reinforcing the need for open datasets, explainable AI, and cross-domain generalisation.