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A Neuro-Symbolic Approach for Anomaly Detection and Complex Fault Diagnosis Exemplified in the Automotive Domain

Published: 05 December 2023 Publication History

Abstract

This paper presents an iterative, hybrid neuro-symbolic approach for anomaly detection and complex fault diagnosis, enabling knowledge-based (symbolic) methods to complement (neural) machine learning methods and vice versa. We demonstrate an instantiation of this novel diagnosis system with applicability in a practically relevant real-world context, specifically the automotive domain. Explainability is indispensable for diagnosis and arises naturally in the system through the specific interplay of neural and symbolic methods. The presented architecture can be considered as a blueprint which is generally transferable to various diagnostic problems and domains.

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  • (2024)Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A ReviewVFAST Transactions on Software Engineering10.21015/vtse.v12i1.171312:1(169-182)Online publication date: 31-Mar-2024
  • (2024)The Emergence of Neuro-Symbolic Artificial IntelligenceNeuro-Symbolic Artificial Intelligence10.1007/978-981-97-8171-3_1(3-15)Online publication date: 23-Dec-2024
  • (2024)Robust Novel Defect Detection with Neurosymbolic AIAdvances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments10.1007/978-3-031-71637-9_26(381-396)Online publication date: 8-Sep-2024

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        cover image ACM Conferences
        K-CAP '23: Proceedings of the 12th Knowledge Capture Conference 2023
        December 2023
        270 pages
        ISBN:9798400701412
        DOI:10.1145/3587259
        • Editors:
        • Brent Venable,
        • Daniel Garijo,
        • Brian Jalaian
        Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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        Published: 05 December 2023

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        Author Tags

        1. Anomaly Detection
        2. Explainable AI
        3. Fault Diagnosis
        4. Knowledge Acquisition
        5. Knowledge Representation
        6. Neuro-Symbolic AI

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        K-CAP '23: Knowledge Capture Conference 2023
        December 5 - 7, 2023
        FL, Pensacola, USA

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        Overall Acceptance Rate 55 of 198 submissions, 28%

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        View all
        • (2024)Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A ReviewVFAST Transactions on Software Engineering10.21015/vtse.v12i1.171312:1(169-182)Online publication date: 31-Mar-2024
        • (2024)The Emergence of Neuro-Symbolic Artificial IntelligenceNeuro-Symbolic Artificial Intelligence10.1007/978-981-97-8171-3_1(3-15)Online publication date: 23-Dec-2024
        • (2024)Robust Novel Defect Detection with Neurosymbolic AIAdvances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments10.1007/978-3-031-71637-9_26(381-396)Online publication date: 8-Sep-2024

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