3,837 research outputs found
Solving stable matching problems using answer set programming
Since the introduction of the stable marriage problem (SMP) by Gale and
Shapley (1962), several variants and extensions have been investigated. While
this variety is useful to widen the application potential, each variant
requires a new algorithm for finding the stable matchings. To address this
issue, we propose an encoding of the SMP using answer set programming (ASP),
which can straightforwardly be adapted and extended to suit the needs of
specific applications. The use of ASP also means that we can take advantage of
highly efficient off-the-shelf solvers. To illustrate the flexibility of our
approach, we show how our ASP encoding naturally allows us to select optimal
stable matchings, i.e. matchings that are optimal according to some
user-specified criterion. To the best of our knowledge, our encoding offers the
first exact implementation to find sex-equal, minimum regret, egalitarian or
maximum cardinality stable matchings for SMP instances in which individuals may
designate unacceptable partners and ties between preferences are allowed.
This paper is under consideration in Theory and Practice of Logic Programming
(TPLP).Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP). arXiv admin note: substantial text overlap with arXiv:1302.725
Modeling Stable Matching Problems with Answer Set Programming
The Stable Marriage Problem (SMP) is a well-known matching problem first
introduced and solved by Gale and Shapley (1962). Several variants and
extensions to this problem have since been investigated to cover a wider set of
applications. Each time a new variant is considered, however, a new algorithm
needs to be developed and implemented. As an alternative, in this paper we
propose an encoding of the SMP using Answer Set Programming (ASP). Our encoding
can easily be extended and adapted to the needs of specific applications. As an
illustration we show how stable matchings can be found when individuals may
designate unacceptable partners and ties between preferences are allowed.
Subsequently, we show how our ASP based encoding naturally allows us to select
specific stable matchings which are optimal according to a given criterion.
Each time, we can rely on generic and efficient off-the-shelf answer set
solvers to find (optimal) stable matchings.Comment: 26 page
Drawing Elena Ferrante's Profile. Workshop Proceedings, Padova, 7 September 2017
Elena Ferrante is an internationally acclaimed Italian novelist whose real identity has been kept secret by E/O publishing house for more than 25 years. Owing to her popularity, major Italian and foreign newspapers have long tried to discover her real identity. However, only a few attempts have been made to foster a scientific debate on her work.
In 2016, Arjuna Tuzzi and Michele Cortelazzo led an Italian research team that conducted a preliminary study and collected a well-founded, large corpus of Italian novels comprising 150 works published in the last 30 years by 40 different authors. Moreover, they shared their data with a select group of international experts on authorship attribution, profiling, and analysis of textual data: Maciej Eder and Jan Rybicki (Poland), Patrick Juola (United States), Vittorio Loreto and his research team, Margherita Lalli and Francesca Tria (Italy), George Mikros (Greece), Pierre Ratinaud (France), and Jacques Savoy (Switzerland).
The chapters of this volume report the results of this endeavour that were first presented during the international workshop Drawing Elena Ferrante's Profile in Padua on 7 September 2017 as part of the 3rd IQLA-GIAT Summer School in Quantitative Analysis of Textual Data. The fascinating research findings suggest that Elena Ferrante\u2019s work definitely deserves \u201cmany hands\u201d as well as an extensive effort to understand her distinct writing style and the reasons for her worldwide success
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