Here you can find some of the research papers I have published in the past years.
29th International Conference on Principles and Practice of Constraint Programming (CP 2023)
In this paper, we describe how we can effectively exploit alternative parameter configurations to a MaxSAT solver. We describe how these configurations can be computed in the context of MaxSAT. In particular, we experimentally show how to easily combine configurations of a non-competitive solver to obtain a better solving approach.
Incomplete MaxSAT approaches for combinatorial testing: Journal of Heuristics volume 28, pages 377–431 (2022)
We present a Satisfiability (SAT)-based approach for building Mixed Covering Arrays with Constraints of minimum length, referred to as the Covering Array Number problem. This problem is central in Combinatorial Testing for the detection of system failures. In particular, we show how to apply Maximum Satisfiability (MaxSAT) technology by describing efficient encodings for different classes of complete and incomplete MaxSAT solvers to compute optimal and suboptimal solutions, respectively. Similarly, we show how to solve through MaxSAT technology a closely related problem, the Tuple Number problem, which we extend to incorporate constraints. For this problem, we additionally provide a new MaxSAT-based incomplete algorithm. The extensive experimental evaluation we carry out on the available Mixed Covering Arrays with Constraints benchmarks and the comparison with state-of-the-art tools confirm the good performance of our approaches.
OptiLog V2: Model, Solve, Tune and Run: International Conference on Theory and Applications of Satisfiability Testing (SAT 2022)
We present an extension of the OptiLog Python framework. We fully redesign the solvers module to support the dynamic loading of incremental SAT solvers with support for external libraries. We introduce new modules for modelling problems into Non-CNF format with support for Pseudo Boolean constraints, for evaluating and parsing the results of applications, and we add support for constrained execution of blackbox programs and SAT-heritage integration. All these enhancements allow OptiLog to become a swiss knife for SAT-based applications in academic and industrial environments.
OptiLog: A Framework for SAT-based Systems: International Conference on Theory and Applications of Satisfiability Testing (SAT 2021)
We present OptiLog, a new Python framework for rapid prototyping of SAT-based systems. OptiLog allows to use and integrate SAT solvers currently developed in C/C++ just by implementing the iSAT C++ interface. It also provides a Python binding to the PBLib C++ toolkit for encoding Pseudo Boolean and Cardinality constraints. Finally, it leverages the power of automatic configurators by allowing to easily create configuration scenarios including multiple solvers and encoders.