2026-07-24T11:53:52Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:22116 2024-11-26T15:16:36Z ddc:004 open_access

Faster Diagnosis with Answer Set Programming (Short Paper) Prikler, Liliana Marie Wotawa, Franz Answer set programming model-based diagnosis performance comparison 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Liliana Marie Prikler and Franz Wotawa 2024 Is Part Of OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.DX.2024.24 urn:nbn:de:0030-drops-221160 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.24 eng https://creativecommons.org/licenses/by/4.0/legalcode