Autonomes Lernen in der sensomotorischen Schleife - ein informationstheoretischer Zugang

• Antragsteller: Professor Dr. Nihat Ay
• Fachliche Zuordnung: Bild- und Sprachverarbeitung, Computergraphik und Visualisierung, Human Computer Interaction, Ubiquitous und Wearable Computing ; Kognitive und systemische Humanneurowissenschaften ; Mathematik ; Statistische Physik, Nichtlineare Dynamik, Komplexe Systeme, Weiche und fluide Materie, Biologische Physik
• Förderung: Förderung von 2011 bis 2018
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 200306544

• Erstellungsjahr: 2019

Zusammenfassung der Projektergebnisse

In summary, the project has achieved its foremost goal to develop geometric ways of designing low-dimesional learning systems. We have a detailed understanding of the geometry of learning systems, based on corresponding embodiment constraints. These systems are modelled in terms of low-dimensional manifolds, which formalises the notion of cheap control within the field of embodied intelligence. Furthermore, we have developed a set on natural measures of morphological computation which provides means for the exploration of body-specific behaviours. Exploration, one of the core themes of the project, was originally intended to be coupled with the above-mentioned low-dimensional manifolds. However, this coupling could not be realised, partly due to the partial funding of the project. On the other hand, the project has initiated a number of research directions, including the subject of exploration, which are pursued by previous members of the project who advanced their careers through the project, and who are now conducting their own independent work as outcome of the project.

Projektbezogene Publikationen (Auswahl)

• A Theory of Cheap Control in Embodied Systems. PLoS Computational Biology (2015) 11(9): e1004427
  G. Montúfar, K. Ghazi-Zahedi, N. Ay
  (Siehe online unter https://doi.org/10.1371/journal.pcbi.1004427)
• Geometric Design Principles for Brains of Embodied Agents. KI - Künstliche Intelligenz (2015) 29: 389–399
  N. Ay
  (Siehe online unter https://doi.org/10.1007/s13218-015-0382-z)
• Geometry and Expressive Power of Conditional Restricted Boltzmann Machines. Journal of Machine Learning Research 16 (2015) 2405–2436
  G. Montúfar, N. Ay, K. Ghazi-Zahedi
  
• Hierarchical Quantification of Synergy in Channels. Frontiers in Robotics and AI (2015)
  P. Perrone, N. Ay
  (Siehe online unter https://doi.org/10.3389/frobt.2015.00035)
• Information Geometry on Complexity and Stochastic Interaction. Entropy (2015) 17(4): 2432–2458
  N. Ay
  (Siehe online unter https://doi.org/10.3390/e17042432)
• The Umwelt of an Embodied Agent – A Measure-Theoretic Definition. Theory in Biosciences (2015) 134: 105–116
  N. Ay, W. Löhr
  (Siehe online unter https://doi.org/10.1007/s12064-015-0217-3)
• Evaluating Morphological Computation in Biomechanical Muscle Models. Frontiers in Robotics and AI (2016)
  K. Ghazi-Zahedi, D. Haeufle, G. Montúfar, S. Schmitt, N. Ay
  (Siehe online unter https://doi.org/10.3389/frobt.2016.00042)
• Comparing Information-Theoretic Measures of Complexity in Boltzmann Machines. Entropy (2017) 19(7): 310
  M.S. Kanwal, J.A. Grochow, N. Ay
  (Siehe online unter https://doi.org/10.3390/e19070310)
• Information Geometry. Ergebnisse der Mathematik und ihrer Grenzgebiete, Springer 2017
  N. Ay, J. Jost, H. V. Lê, and L. Schwachhöfer
  (Siehe online unter https://doi.org/10.1007/978-3-319-56478-4)
• Morphological Computation: Synergy of Body and Brain. Entropy (2017) 19(9): 456
  K. Ghazi-Zahedi, C. Langer, N. Ay
  (Siehe online unter https://doi.org/10.3390/e19090456)
• Morphological Intelligence – Measuring the Body’s Contribution to Intelligence. Cham: Springer 2019. ISBN 978-3-030-20621-5
  Keyan Ghazi-Zahedi
  (Siehe online unter https://doi.org/10.1007/978-3-030-20621-5)