145 research outputs found
ASlib: A Benchmark Library for Algorithm Selection
The task of algorithm selection involves choosing an algorithm from a set of
algorithms on a per-instance basis in order to exploit the varying performance
of algorithms over a set of instances. The algorithm selection problem is
attracting increasing attention from researchers and practitioners in AI. Years
of fruitful applications in a number of domains have resulted in a large amount
of data, but the community lacks a standard format or repository for this data.
This situation makes it difficult to share and compare different approaches
effectively, as is done in other, more established fields. It also
unnecessarily hinders new researchers who want to work in this area. To address
this problem, we introduce a standardized format for representing algorithm
selection scenarios and a repository that contains a growing number of data
sets from the literature. Our format has been designed to be able to express a
wide variety of different scenarios. Demonstrating the breadth and power of our
platform, we describe a set of example experiments that build and evaluate
algorithm selection models through a common interface. The results display the
potential of algorithm selection to achieve significant performance
improvements across a broad range of problems and algorithms.Comment: Accepted to be published in Artificial Intelligence Journa
An Enhanced Features Extractor for a Portfolio of Constraint Solvers
Recent research has shown that a single arbitrarily efficient solver can be
significantly outperformed by a portfolio of possibly slower on-average
solvers. The solver selection is usually done by means of (un)supervised
learning techniques which exploit features extracted from the problem
specification. In this paper we present an useful and flexible framework that
is able to extract an extensive set of features from a Constraint
(Satisfaction/Optimization) Problem defined in possibly different modeling
languages: MiniZinc, FlatZinc or XCSP. We also report some empirical results
showing that the performances that can be obtained using these features are
effective and competitive with state of the art CSP portfolio techniques
An Extensive Evaluation of Portfolio Approaches for Constraint Satisfaction Problems
In the context of Constraint Programming, a portfolio
approach exploits the complementary strengths of a portfolio of
different constraint solvers. The goal is to predict and run the best
solver(s) of the portfolio for solving a new, unseen problem. In
this work we reproduce, simulate, and evaluate the performance
of different portfolio approaches on extensive benchmarks of
Constraint Satisfaction Problems. Empirical results clearly show
the benefits of portfolio solvers in terms of both solved instances
and solving time
An Extensive Evaluation of Portfolio Approaches for Constraint Satisfaction Problems
International audienceIn the context of Constraint Programming, a portfolio approach exploits the complementary strengths of a portfolio of different constraint solvers. The goal is to predict and run the best solver(s) of the portfolio for solving a new, unseen problem. In this work we reproduce, simulate, and evaluate the performance of different portfolio approaches on extensive benchmarks of Constraint Satisfaction Problems. Empirical results clearly show the benefits of portfolio solvers in terms of both solved instances and solving time
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
Automatic construction of parallel portfolios via algorithm configuration
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