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1st International Round Table on Intelligent Control for Space Missions

24 November 2017
ESTEC
Europe/Amsterdam timezone
Artificial Intelligence for Space

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Session

Session 2

24 Nov 2017, 11:20
Newton 2 (ESTEC)

Newton 2

ESTEC

Keplerlaan 1, AG2200 Noordwijk, The Netherlands

Conveners

Session 2: Late Morning

  • Dominika Perz (ESA)

Presentation materials

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  1. 23. "Optimisation Technology for Intelligent Control" by University of Southampton
    Prof. Joerg Fliege (University of Southampton)
    24/11/2017, 11:20
    Optimisation is a key technology for intelligent control, as exemplified by evolutionary and genetic algorithms, machine learning techniques, and training of neural networks. Deterministic global optimisation is concerned with rigorous methods to find globally optimal solutions for such applications as well as mathematically precise error bounds for such solutions. Corresponding solver...
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  2. 9. "Challenges and state-of-the-art of neural network verification" by fortiss GmbH
    Georg Nührenberg (fortiss GmbH)
    24/11/2017, 11:45
    Neural Networks Control Systems
    The deployment of Artificial Neural Networks (ANNs) in critical missions poses a number of new verification challenges. In particular, for ANN-enabled perception and control it is important to establish properties about the resilience of ANNs to noisy or even maliciously manipulated sensory input. Furthermore, given interpretability of the inputs and outputs of an ANN, certain safety...
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  3. 10. "Applying Artificial Intelligence techniques to the orbit propagation problem" by University of La Rioja
    Dr Juan Félix San-Juan (Scientific Computing Group (GRUCACI), University of La Rioja)
    24/11/2017, 12:10
    Genetic and Evolutionary Computation
    Artificial Intelligence in general, and machine learning in particular, are the cornerstones of the so-called Industry 4.0. Being aware of the new opportunities provided by current technology and increased computational power, public administrations are fostering this upcoming fourth industrial revolution, in which both the private sector and academia are also taking an active part. The space...
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  4. 11. "Deep Reinforcement Learning for Control" by the University of Stuttgart
    Prof. Daniel Hennes (University of Stuttgart)
    24/11/2017, 12:35
    Intelligent Control for Transportation
    Deep learning techniques allow us to scale reinforcement learning to problems that were previously intractable, i.e. to domains with high-dimensional state (or observation) spaces and continuous action spaces. We will give an overview of state-of-the-art deep reinforcement learning methods, including deep Q-learning, deep deterministic policy gradients, and asynchronous...
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