16 research outputs found
sunny-as2: Enhancing SUNNY for Algorithm Selection
SUNNY is an Algorithm Selection (AS) technique originally tailored for
Constraint Programming (CP). SUNNY enables to schedule, from a portfolio of
solvers, a subset of solvers to be run on a given CP problem. This approach has
proved to be effective for CP problems, and its parallel version won many gold
medals in the Open category of the MiniZinc Challenge -- the yearly
international competition for CP solvers. In 2015, the ASlib benchmarks were
released for comparing AS systems coming from disparate fields (e.g., ASP, QBF,
and SAT) and SUNNY was extended to deal with generic AS problems. This led to
the development of sunny-as2, an algorithm selector based on SUNNY for ASlib
scenarios. A preliminary version of sunny-as2 was submitted to the Open
Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the
best approach for the runtime minimization of decision problems. In this work,
we present the technical advancements of sunny-as2, including: (i)
wrapper-based feature selection; (ii) a training approach combining feature
selection and neighbourhood size configuration; (iii) the application of nested
cross-validation. We show how sunny-as2 performance varies depending on the
considered AS scenarios, and we discuss its strengths and weaknesses. Finally,
we also show how sunny-as2 improves on its preliminary version submitted to
OASC
Understanding and Extending Incremental Determinization for 2QBF
Incremental determinization is a recently proposed algorithm for solving
quantified Boolean formulas with one quantifier alternation. In this paper, we
formalize incremental determinization as a set of inference rules to help
understand the design space of similar algorithms. We then present additional
inference rules that extend incremental determinization in two ways. The first
extension integrates the popular CEGAR principle and the second extension
allows us to analyze different cases in isolation. The experimental evaluation
demonstrates that the extensions significantly improve the performance
Efficient local search for Pseudo Boolean Optimization
Algorithms and the Foundations of Software technolog