Detailed Information on Publication Record
2016
Tunable Online MUS/MSS Enumeration
BENDÍK, Jaroslav, Nikola BENEŠ, Ivana ČERNÁ and Jiří BARNATBasic information
Original name
Tunable Online MUS/MSS Enumeration
Authors
BENDÍK, Jaroslav (203 Czech Republic, guarantor), Nikola BENEŠ (203 Czech Republic, belonging to the institution), Ivana ČERNÁ (203 Czech Republic, belonging to the institution) and Jiří BARNAT (203 Czech Republic, belonging to the institution)
Edition
65. vyd. Dagstuhl, Germany, Foundations of Software Technology and Theoretical Computer Science - 36th International Conference, FSTTCS 2016, p. 661-673, 13 pp. 2016
Publisher
Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Germany
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
printed version "print"
RIV identification code
RIV/00216224:14330/16:00091546
Organization unit
Faculty of Informatics
ISBN
978-3-95977-027-9
ISSN
Keywords in English
Minimal unsatisfiable subsets; Maximal satisfiable subsets; Unsatisfiability analysis; Infeasibility analysis
Tags
International impact, Reviewed
Změněno: 13/10/2020 09:44, prof. RNDr. Ivana Černá, CSc.
Abstract
V originále
In various areas of computer science, the problem of dealing with a set of constraints arises. If the set of constraints is unsatisfiable, one may ask for a minimal description of the reason for this unsatisifiability. Minimal unsatisfiable subsets (MUSes) and maximal satisfiable subsets (MSSes) are two kinds of such minimal descriptions. The goal of this work is the enumeration of MUSes and MSSes for a given constraint system. As such full enumeration may be intractable in general, we focus on building an online algorithm, which produces MUSes/MSSes in an on-the-fly manner as soon as they are discovered. The problem has been studied before even in its online version. However, our algorithm uses a novel approach that is able to outperform the current state-of-the-art algorithms for online MUS/MSS enumeration. Moreover, the performance of our algorithm can be adjusted using tunable parameters. We evaluate the algorithm on a set of benchmarks.
Links
MUNI/A/0935/2015, interní kód MU |
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MUNI/A/0945/2015, interní kód MU |
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692474, interní kód MU |
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