Summary
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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Sources and literature
Bengio Y (2009) Learning Deep Architectures for AI, Foundations and Trends in Machine Learning 2 (1) 1-127
Krizhevsky A, Sutskever I, Hinton G (2012) ImageNet Classification with Deep Convolutional Neural Networks, in Proc. NIPS
Hinton G, Deng L, Yu D, Dahl G, Mohamed A and Jaitly N (2012) Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups, IEEE Signal Processing Magazine 29 (6) 82-97
Mikolov T, Sutskever I, Chen K, Corrado G and Dean J (2013) Distributed Representations of Words and Phrases and their Compositionality in Proc. NIPS
Levine S, Wagener N, Abbeel P (2015) Learning Contact-Rich Manipulation Skills with Guided Policy Search, Proc. IEEE ICRA
Esteva A, Kuprel B, Novoa R, Ko J, Swetter S, Blau H, Thrun S (2017) Dermatologistlevel Classification of Skin Cancer with Deep Neural Networks, Nature 542 (7639) 115-118
Silver D, Huang A, Maddison C, Guez A, Sifre L, van den Driesche G, Schrittwieser J (2016) „Mastering the Game of Go with Deep Neural Networks and Tree Search“ Nature 529 (7587) 484-489
Moracik M, Schmidt M, Burch N, Lisy V, Morrill D, Bard N, Davis T, Waugh K, Johanson M and Bowling M (2017) DeepStack: Expert-level Artificial Intelligence in Heads-up No-limit Poker, Science 356 (6337) 508-513
Hornik K, Stinchcombe M, White H (1989) Multilayer Feedforward Networks Are Universal Approximators, Neural Networks 2 (5) 359-366
Cybenko G (1989) Approximation by Superpositions of a Sigmoidal Function, Mathematics of Control, Signals and Systems 2 (4) 303-314
Rumelhart D, Hinton G and Williams R (1986) Learning Representations by Back- propagating Errors, Nature 323 (9) 533-536
V. Vapnik and A. Chervonenkis (1971) On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities, Theory of Probability and its Applications 16 (2) 264-280
Barker J, Bulin J, Hamaekers J, Mathias S, LC-GAP (2017) Localized Coulomb Descriptors for the Gaussian Approximation Potential, in Scientific Computing and Algorithms in Industrial Simulations, Griebel, Michael, Schüller, Anton, Schweitzer, Marc Alexander (eds.), Springer
Garcke J, Iza-Teran R, Prabakaran N (2016) Datenanalysemethoden zur Auswertung von Simulationsergebnissen im Crash und deren Abgleich mit dem Experiment, VDI-Tagung SIMVEC 2016.
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Bauckhage, C., Bauernhansl, T., Beyerer, J., Garcke, J. (2019). Cognitive Systems and Robotics. In: Neugebauer, R. (eds) Digital Transformation. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58134-6_14
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DOI: https://doi.org/10.1007/978-3-662-58134-6_14
Publisher Name: Springer Vieweg, Berlin, Heidelberg
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