Applying algorithm selection to abductive diagnostic reasoning

Research output: Contribution to journalArticleResearchpeer-review

Abstract

The complexity of technical systems requires increasingly advanced fault diagnosis methods to ensure safety and reliability during operation. Particularly in domains where maintenance constitutes an extensive portion of the entire operation cost, efficient and effective failure identification holds the potential to provide large economic value. Abduction offers an intuitive concept for diagnostic reasoning relying on the notion of logical entailment. Nevertheless, abductive reasoning is an intractable problem and computing solutions for instances of reasonable size and complexity persists to pose a challenge. In this paper, we investigate algorithm selection as a mechanism to predict the ``best'' performing technique for a specific abduction scenario within the framework of model-based diagnosis. Based on a set of structural attributes extracted from the system models, our meta-approach trains a machine learning classifier that forecasts the most runtime efficient abduction technique given a new diagnosis problem. To assess the predictor's selection capabilities and the suitability of the meta-approach in general, we conducted an empirical analysis featuring seven abductive reasoning approaches. The results obtained indicate that applying algorithm selection is competitive in comparison to always choosing a single abductive reasoning method.
LanguageEnglish
Pages 1–19
Number of pages19
JournalApplied Intelligence
DOIs
StatusPublished - 1 May 2018

Fingerprint

Failure analysis
Learning systems
Classifiers
Economics
Costs

Fields of Expertise

  • Information, Communication & Computing

Cite this

Applying algorithm selection to abductive diagnostic reasoning. / Koitz-Hristov, Roxane; Wotawa, Franz.

In: Applied Intelligence, 01.05.2018, p. 1–19.

Research output: Contribution to journalArticleResearchpeer-review

@article{3dca217cfb2b4770baac35da869fd675,
title = "Applying algorithm selection to abductive diagnostic reasoning",
abstract = "The complexity of technical systems requires increasingly advanced fault diagnosis methods to ensure safety and reliability during operation. Particularly in domains where maintenance constitutes an extensive portion of the entire operation cost, efficient and effective failure identification holds the potential to provide large economic value. Abduction offers an intuitive concept for diagnostic reasoning relying on the notion of logical entailment. Nevertheless, abductive reasoning is an intractable problem and computing solutions for instances of reasonable size and complexity persists to pose a challenge. In this paper, we investigate algorithm selection as a mechanism to predict the ``best'' performing technique for a specific abduction scenario within the framework of model-based diagnosis. Based on a set of structural attributes extracted from the system models, our meta-approach trains a machine learning classifier that forecasts the most runtime efficient abduction technique given a new diagnosis problem. To assess the predictor's selection capabilities and the suitability of the meta-approach in general, we conducted an empirical analysis featuring seven abductive reasoning approaches. The results obtained indicate that applying algorithm selection is competitive in comparison to always choosing a single abductive reasoning method.",
author = "Roxane Koitz-Hristov and Franz Wotawa",
year = "2018",
month = "5",
day = "1",
doi = "10.1007/s10489-018-1171-9",
language = "English",
pages = "1–19",
journal = "Applied Intelligence",
issn = "0924-669X",
publisher = "Springer Netherlands",

}

TY - JOUR

T1 - Applying algorithm selection to abductive diagnostic reasoning

AU - Koitz-Hristov, Roxane

AU - Wotawa, Franz

PY - 2018/5/1

Y1 - 2018/5/1

N2 - The complexity of technical systems requires increasingly advanced fault diagnosis methods to ensure safety and reliability during operation. Particularly in domains where maintenance constitutes an extensive portion of the entire operation cost, efficient and effective failure identification holds the potential to provide large economic value. Abduction offers an intuitive concept for diagnostic reasoning relying on the notion of logical entailment. Nevertheless, abductive reasoning is an intractable problem and computing solutions for instances of reasonable size and complexity persists to pose a challenge. In this paper, we investigate algorithm selection as a mechanism to predict the ``best'' performing technique for a specific abduction scenario within the framework of model-based diagnosis. Based on a set of structural attributes extracted from the system models, our meta-approach trains a machine learning classifier that forecasts the most runtime efficient abduction technique given a new diagnosis problem. To assess the predictor's selection capabilities and the suitability of the meta-approach in general, we conducted an empirical analysis featuring seven abductive reasoning approaches. The results obtained indicate that applying algorithm selection is competitive in comparison to always choosing a single abductive reasoning method.

AB - The complexity of technical systems requires increasingly advanced fault diagnosis methods to ensure safety and reliability during operation. Particularly in domains where maintenance constitutes an extensive portion of the entire operation cost, efficient and effective failure identification holds the potential to provide large economic value. Abduction offers an intuitive concept for diagnostic reasoning relying on the notion of logical entailment. Nevertheless, abductive reasoning is an intractable problem and computing solutions for instances of reasonable size and complexity persists to pose a challenge. In this paper, we investigate algorithm selection as a mechanism to predict the ``best'' performing technique for a specific abduction scenario within the framework of model-based diagnosis. Based on a set of structural attributes extracted from the system models, our meta-approach trains a machine learning classifier that forecasts the most runtime efficient abduction technique given a new diagnosis problem. To assess the predictor's selection capabilities and the suitability of the meta-approach in general, we conducted an empirical analysis featuring seven abductive reasoning approaches. The results obtained indicate that applying algorithm selection is competitive in comparison to always choosing a single abductive reasoning method.

U2 - 10.1007/s10489-018-1171-9

DO - 10.1007/s10489-018-1171-9

M3 - Article

SP - 1

EP - 19

JO - Applied Intelligence

T2 - Applied Intelligence

JF - Applied Intelligence

SN - 0924-669X

ER -