Maximal-confirmation diagnoses

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Models used for Model-Based Diagnosis usually assume that observations, and predictions based on the system description are accurate. In some domains, however, this assumption is invalid. Observations may not be accurate or the behavior model of the system does not allow for accurate predictions. Therefore, the accuracy of predictions, which is a function of the accuracy of the observed system inputs and the behavior model of the system, may differ from the accuracy of the observed system outputs. This paper investigates the consequences of using inaccurate values.(1) The paper will show that traditional notions of preferred diagnoses such as abductive diagnosis and minimum consistency-based diagnosis are no longer suited if the available data has different accuracies. A new notion of preferred diagnoses, called maximal-confirmation diagnoses, is introduced.
Original languageEnglish
Pages (from-to)467-477
Number of pages11
JournalKnowledge-Based Systems
Volume24
DOIs
Publication statusPublished - 2011

Cite this

@article{595136e565654766a94698542aea8d6a,
title = "Maximal-confirmation diagnoses",
abstract = "Models used for Model-Based Diagnosis usually assume that observations, and predictions based on the system description are accurate. In some domains, however, this assumption is invalid. Observations may not be accurate or the behavior model of the system does not allow for accurate predictions. Therefore, the accuracy of predictions, which is a function of the accuracy of the observed system inputs and the behavior model of the system, may differ from the accuracy of the observed system outputs. This paper investigates the consequences of using inaccurate values.(1) The paper will show that traditional notions of preferred diagnoses such as abductive diagnosis and minimum consistency-based diagnosis are no longer suited if the available data has different accuracies. A new notion of preferred diagnoses, called maximal-confirmation diagnoses, is introduced.",
author = "Nico Roos",
note = "DIO: 10.1016/j.knosys.2010.12.002",
year = "2011",
doi = "10.1016/j.knosys.2010.12.002",
language = "English",
volume = "24",
pages = "467--477",
journal = "Knowledge-Based Systems",
issn = "0950-7051",
publisher = "Elsevier Science",

}

Maximal-confirmation diagnoses. / Roos, Nico.

In: Knowledge-Based Systems, Vol. 24, 2011, p. 467-477.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Maximal-confirmation diagnoses

AU - Roos, Nico

N1 - DIO: 10.1016/j.knosys.2010.12.002

PY - 2011

Y1 - 2011

N2 - Models used for Model-Based Diagnosis usually assume that observations, and predictions based on the system description are accurate. In some domains, however, this assumption is invalid. Observations may not be accurate or the behavior model of the system does not allow for accurate predictions. Therefore, the accuracy of predictions, which is a function of the accuracy of the observed system inputs and the behavior model of the system, may differ from the accuracy of the observed system outputs. This paper investigates the consequences of using inaccurate values.(1) The paper will show that traditional notions of preferred diagnoses such as abductive diagnosis and minimum consistency-based diagnosis are no longer suited if the available data has different accuracies. A new notion of preferred diagnoses, called maximal-confirmation diagnoses, is introduced.

AB - Models used for Model-Based Diagnosis usually assume that observations, and predictions based on the system description are accurate. In some domains, however, this assumption is invalid. Observations may not be accurate or the behavior model of the system does not allow for accurate predictions. Therefore, the accuracy of predictions, which is a function of the accuracy of the observed system inputs and the behavior model of the system, may differ from the accuracy of the observed system outputs. This paper investigates the consequences of using inaccurate values.(1) The paper will show that traditional notions of preferred diagnoses such as abductive diagnosis and minimum consistency-based diagnosis are no longer suited if the available data has different accuracies. A new notion of preferred diagnoses, called maximal-confirmation diagnoses, is introduced.

U2 - 10.1016/j.knosys.2010.12.002

DO - 10.1016/j.knosys.2010.12.002

M3 - Article

VL - 24

SP - 467

EP - 477

JO - Knowledge-Based Systems

JF - Knowledge-Based Systems

SN - 0950-7051

ER -