Relaxed Abduction : Robust Information Interpretation for Industrial Applications / by Thomas Hubauer

By:

Hubauer, Thomas [aut]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink BücherPublisher: Wiesbaden ; s.l. : Springer Fachmedien Wiesbaden, 2016Description: Online-Ressource (XVII, 134 p. 11 illus., 1 illus. in color, online resource)ISBN:

9783658144074

Subject(s):

Additional physical formats: 9783658144067 | Druckausg.: 978-3-658-14406-7 MSC: MSC: *68-02 | 05C65 | 68T27 | 68T30LOC classification:

QA76.9.M35

DOI: DOI: 10.1007/978-3-658-14407-4Online resources:

Summary: Formalizing Diagnostics Using Description Logics -- Formalization and Properties of Relaxed Abduction -- Solving Diagnostic Problems Using Relaxed Abduction -- Incremental Diagnostics.Summary: Thomas Hubauer addresses the challenge of providing reasonable interpretations of incomplete observational data in the context of imperfect domain models – a situation often encountered in the context of industrial diagnostics. To tackle this problem, the author proposes a novel approach called Relaxed Abduction, which is able to derive pragmatic interpretations in situations where existing methods either fail or provide overly complex solutions. To strengthen the link to applications in industrial diagnostics, he develops a methodology to structure diagnosis problems according to ISO 13379 and express it using multiple description logic knowledge bases. Contents Formalizing Diagnostics Using Description Logics Formalization and Properties of Relaxed Abduction Solving Diagnostic Problems Using Relaxed Abduction Incremental Diagnostics Target Groups Researchers and students of computer science, knowledge management, and applied mathematics Practitioners in the area of industrial diagnostics and maintenance The Author Thomas Hubauer is a Research Scientist at the R&D department of a large German engineering company. His topics of interest range from ontologies and reasoning to machine learning and data analytics.PPN: PPN: 1657695255Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SXCS | ZDB-2-SCS

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