Talks and Poster Presentations (with Proceedings-Entry):

A. Ignatiev, A. Morgado, J. Marques-Silva, G. Weissenbacher:
"Model-Based Diagnosis with Multiple Observations";
Talk: IJCAI 2019 - 28th International Joint Conference on Artificial Intelligence, Macao, China; 2019-08-10 - 2019-08-16; in: "International Joint Conference on Artificial Intelligence", (2019), ISBN: 978-0-9992411-4-1; 1108 - 1115.

English abstract:
Existing automated testing frameworks require multiple observations to be jointly diagnosed with the purpose of identifying common fault locations. This is the case for example with continuous integration tools. This paper shows that existing solutions fail to compute the set of minimal diagnoses, and as a result run times can increase by orders of magnitude. The paper proposes not only solutions to correct existing algorithms, but also conditions for improving their run times. Nevertheless, the diagnosis of multiple observations raises a number of important computational challenges, which even the corrected algorithms are often unable to cope with. As a result, the paper devises a novel algorithm for diagnosing multiple observations, which is shown to enable significant performance improvements in practice

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.