about as...

The AI in dynamic action (AIDA) scientific group is dedicated to the study and application of artificial intelligence in dynamical environments, with a special focus on safety and trustworthiness. Among various machine learning approaches, we are especially interested in reinforcement learning - a methodology resembling the action of living beings in changing, uncertain environments which react by punishment and reward. Applied to human economy, AI has to fulfill requirements on safety and trustworthiness, especially regarding the personal data privacy. These requirements become particularly challenging in dynamical application of AI, such as robotic, autonomous driving, medical therapy support, chemical engineering, energy management etc.
The scientific group seeks to apply an interdisciplinary approach by the fusion of machine learning with various fields, such as system and control theory, to develop novel dynamic AI methods.

The team of the scientific group

Projects & research

Publications

Beckenbach, P. Osinenko and S. Streif.

A Q-learning predictive control scheme with guaranteed stability.

European Journal of Control 56 (2020): 167-178

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Beckenbach, P. Osinenko and S. Streif.

On closed-loop stability of model predictive controllers with learning costs.

European Control Conference, 2020

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Osinenko, A. Kobelski and S. Streif.

A method of online traction parameter identification and mapping.

IFAC World Congress, 2020

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Osinenko, L. Beckenbach, T. Göhrt and S. Streif.

A reinforcement learning method with real-time closed-loop stability guarantee.

IFAC World Congress, 2020

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Russwurm, P. Osinenko and S. Streif.

Optimal control of centrifugal spreader.

IFAC World Congress, 2020

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Media

This image for Nejroset' nauchili opredelyat' optimal'nyj srok hraneniya fruktov

Нейросеть научили определять оптимальный срок хранения фруктов

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