Detailed Information on Publication Record
2020
MUST: Minimal Unsatisfiable Subsets Enumeration Tool
BENDÍK, Jaroslav and Ivana ČERNÁBasic information
Original name
MUST: Minimal Unsatisfiable Subsets Enumeration Tool
Authors
BENDÍK, Jaroslav (203 Czech Republic, guarantor, belonging to the institution) and Ivana ČERNÁ (203 Czech Republic, belonging to the institution)
Edition
Neuveden, Tools and Algorithms for the Construction and Analysis of Systems, p. 135-152, 18 pp. 2020
Publisher
Springer International Publishing
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10200 1.2 Computer and information sciences
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
printed version "print"
Impact factor
Impact factor: 0.402 in 2005
RIV identification code
RIV/00216224:14330/20:00115396
Organization unit
Faculty of Informatics
ISBN
978-3-030-45189-9
ISSN
Keywords in English
Minimal unsatisfiable subsets;Unsatisfiability analysis;Infeasibility analysis;MUS;Diagnosis
Tags
International impact, Reviewed
Změněno: 10/5/2021 05:40, RNDr. Pavel Šmerk, Ph.D.
Abstract
V originále
In many areas of computer science, we are given an unsatisfiable set of constraints with the goal to provide an insight into the unsatisfiability. One of common approaches is to identify minimal unsatisfiable subsets (MUSes) of the constraint set. The more MUSes are identified, the better insight is obtained. However, since there can be up to exponentially many MUSes, their complete enumeration might be intractable. Therefore, we focus on algorithms that enumerate MUSes online, i.e. one by one, and thus can find at least some MUSes even in the intractable cases. Since MUSes find applications in different constraint domains and new applications still arise, there have been proposed several domain agnostic algorithms. Such algorithms can be applied in any constraint domain and thus theoretically serve as ready-to-use solutions for all the emerging applications. However, there are almost no domain agnostic tools, i.e. tools that both implement domain agnostic algorithms and can be easily extended to support any constraint domain. In this work, we close this gap by introducing a domain agnostic tool called MUST. Our tool outperforms other existing domain agnostic tools and moreover, it is even competitive to fully domain specific solutions.
Links
EF16_019/0000822, research and development project |
| ||
MUNI/A/1050/2019, interní kód MU |
| ||
MUNI/A/1076/2019, interní kód MU |
|