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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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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Grundlagen des Maschinellen Lernens

2021 , Bauckhage, Christian , Hübner, Wolfgang , Hug, Ronny , Paaß, Gerhard , Rüping, Stefan

Zu definieren, was die menschliche Intelligenz sowie intelligentes Handeln – und da­mit auch die Künstliche Intelligenz – ausmacht, ist außerordentlich schwer und be­schäftigt Philosophen und Psychologen seit Jahrtausenden. Allgemein anerkannt istaber, dass die Fähigkeit zu lernen ein zentrales Merkmal vonIntelligenzist. So ist auchdas Forschungsgebiet desMaschinellen Lernens(engl.machine learning, ML) ein zen­traler Teil der Künstlichen Intelligenz, das hinter vielen aktuellen Erfolgen von KI-Sys­temen steckt.

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Cognitive Systems and Robotics

2019 , Bauckhage, Christian , Bauernhansl, Thomas , Beyerer, Jürgen , Garcke, Jochen

Cognitive systems are able to monitor and analyze complex processes, which also provides them with the ability to make the right decisions in unplanned or unfamiliar situations. Fraunhofer experts are employing machine learning techniques to harness new cognitive functions for robots and automation solutions. To do this, they are equipping systems with technologies that are inspired by human abilities, or imitate and optimize them. This report describes these technologies, illustrates current example applications, and lays out scenarios for future areas of application.

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Tiefe neuronale Netze

2021 , Bauckhage, Christian , Hübner, Wolfgang , Hug, Ronny , Paaß, Gerhard

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Kognitive Systeme und Robotik

2018 , Bauckhage, Christian , Bauernhansl, Thomas , Beyerer, Jürgen , Garcke, Jochen

Kognitive Systeme können komplexe Prozesse überwachen, analysieren und gewinnen daraus auch die Fähigkeit, in ungeplanten oder unbekannten Situationen richtig zu entscheiden. Fraunhofer-Experten setzen Verfahren des maschinellen Lernens ein, um neue kognitive Funktionen für Roboter und Automatisierungslösungen zu nutzen. Dazu statten sie Systeme mit Technologien aus, die von menschlichen Fähigkeiten inspiriert sind bzw. diese imitieren und optimieren. Der Bericht beschreibt diese Technologien, erläutert aktuelle Anwendungsbeispiele und entwirft Szenarien für zukünftige Anwendungsfelder.

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