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Documents authored by Prikler, Liliana Marie


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Short Paper
Faster Diagnosis with Answer Set Programming (Short Paper)

Authors: Liliana Marie Prikler and Franz Wotawa

Published in: OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)


Abstract
From hardware to software to human patients, diagnosis has been one of the first areas of interest in artificial intelligence, and has remained a relevant topic since. Recent research in model-based diagnosis has shown that answer set programming not only allows for an easy expression of diagnosis problems, but also efficient solving. In this paper, we improve on previous results by making use of various modern answer set programming techniques. Our experiments compare multi-shot solving, heuristics and preferences, with results indicating that heuristics provide the fastest solutions on most instances we studied.

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Liliana Marie Prikler and Franz Wotawa. Faster Diagnosis with Answer Set Programming (Short Paper). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 24:1-24:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)


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@InProceedings{prikler_et_al:OASIcs.DX.2024.24,
  author =	{Prikler, Liliana Marie and Wotawa, Franz},
  title =	{{Faster Diagnosis with Answer Set Programming}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{24:1--24:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.24},
  URN =		{urn:nbn:de:0030-drops-221160},
  doi =		{10.4230/OASIcs.DX.2024.24},
  annote =	{Keywords: Answer set programming, model-based diagnosis, performance comparison}
}
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