Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems

2023 , Rueden, Laura von , Mayer, Sebastian , Beckh, Katharina , Georgiev, Bogdan , Giesselbach, Sven , Heese, Raoul , Kirsch, Birgit , Walczak, Michal , Pfrommer, Julius , Pick, Annika , Ramamurthy, Rajkumar , Garcke, Jochen , Bauckhage, Christian , Schuecker, Jannis

Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.

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KI-Engineering in der Produktion

2023 , Frey, Christian , Goßmann, Ann-Kathrin , Hasterok, Constanze , Hertweck, Philipp , Kühnert, Christian , Pfrommer, Julius , Usländer, Thomas , Ernis, Gunar , Hecker, Dirk , Poretschkin, Maximilian , Schulz, Daniel , Wegener, Dennis , Wirtz, Tim , Zimmermann, Alexander , Usländer, Thomas , Schulz, Daniel

Um Methoden der künstlichen Intelligenz (KI) in IT-Systemen der industriellen Produktion nachhaltig und operativ einzusetzen, bedarf es der Methodik des KI-Engineering. KI-Engineering adressiert die systematische Entwicklung und den Betrieb von KI-basierten Lösungen als Teil von Systemen, die komplexe Aufgaben erfüllen. Ziel ist es, das Innovations- und Optimierungspotenzial von KI-Verfahren in der industriellen Produktion nutzen zu können. Die Studie spannt die Dimensionen für KI-Engineering-Anwendungen auf, umreißt die qualitativen Anforderungen in der Entwicklung und im Betrieb unter dem Blickwinkel des Anwenders und Entscheiders. Verschiedene Anwendungsfälle werden in vier Autonomiestufen eingeordnet: von KI-basierten Assistenzfunktionen bis hin zu autonomen und adaptiven Systemen. Zudem werden passende Lösungsmethoden aufgezeigt. Ein Kapitel widmet sich den technischen und organisatorischen Schulden beim Einsatz von KI-Methoden. Hierin wird als Antwort das KI-Engineering-Vorgehensmodell PAISE® im Kontext bestehender Modelle aus dem Data Mining und dem Software-Engineering erläutert. Im Anschluss werden relevante Initiativen und Projekte beschrieben und anstehende Entwicklungen umrissen.

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Publication

Informed Machine Learning - A Taxonomy and Survey of Integrating Knowledge into Learning Systems

2019-03-29 , Rüden, Laura von , Mayer, Sebastian , Beckh, Katharina , Georgiev, Bogdan , Giesselbach, Sven , Heese, Raoul , Kirsch, Birgit , Pfrommer, Julius , Pick, Annika , Ramamurthy, Rajkumar , Schuecker, Jannis , Garcke, Jochen , Bauckhage, Christian , Walczak, Michal

Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process, which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. First, we provide a definition and propose a concept for informed machine learning, which illustrates its building blocks and distinguishes it from conventional machine learning. Second, we introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Third, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.

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