Faster Diagnosis with Answer Set Programming

Liliana Marie Prikler; Franz Wotawa

doi:10.4230/OASICS.DX.2024.24

Faster Diagnosis with Answer Set Programming

Liliana Marie Prikler, Franz Wotawa

Institute of Software Engineering and Artificial Intelligence (7160)

Publikation: Konferenzbeitrag › Paper › Begutachtung

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.

Originalsprache	englisch
Seiten	24:1-24:13
DOIs	https://doi.org/10.4230/OASICS.DX.2024.24
Publikationsstatus	Veröffentlicht - 26 Nov. 2024
Veranstaltung	35th International Conference on Principles of Diagnosis and Resilient Systems, DX 2024 - Vienna, Österreich Dauer: 4 Nov. 2024 → 7 Nov. 2024

Konferenz

Konferenz	35th International Conference on Principles of Diagnosis and Resilient Systems, DX 2024
Land/Gebiet	Österreich
Ort	Vienna
Zeitraum	4/11/24 → 7/11/24

ASJC Scopus subject areas

Geografie, Planung und Entwicklung
Modellierung und Simulation

Zugriff auf Dokument

10.4230/OASICS.DX.2024.24Lizenz: CC BY 4.0

https://dblp.org/rec/conf/dx/PriklerW24Lizenz: CC BY 4.0

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

AI4CSM - Automotive Intelligenz für/mit Connected Shared Mobilität
Wotawa, F. (Teilnehmer (Co-Investigator))
1/05/21 → 30/11/24
Projekt: Forschungsprojekt

Dieses zitieren

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title = "Faster Diagnosis with Answer Set Programming",

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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PY - 2024/11/26

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N2 - 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.

AB - 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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KW - performance comparison

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T2 - 35th International Conference on Principles of Diagnosis and Resilient Systems, DX 2024

Y2 - 4 November 2024 through 7 November 2024

ER -

Faster Diagnosis with Answer Set Programming

Abstract

Konferenz

ASJC Scopus subject areas

Zugriff auf Dokument

Andere Dateien und Links

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Projekte

AI4CSM - Automotive Intelligenz für/mit Connected Shared Mobilität

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