Cybernetic Learning Systems Group
“Cybernetics is the study of interaction and feedback processes, which drives our approach to developing AI solutions for complex systems. We develop AI agents which can interact with their environment, communicate and cooperate with other agents, and learn from feedback. Combining human expertise and creativity with the processing power of AI allows us to create new solutions for the problems of today and tomorrow.”
Fields of Expertise
- ML for autonomous systems & robots
- Methodical focus on RL
- Reinforcement Learning
- AI-supported decision making
- Intelligent transportation systems and aviation
- Human-AI interaction and co-learning
- Embodied AI
- Multi-agent systems
Our group boasts a wide range of expertise, given the diverse backgrounds of our team members. We develop AI systems which have a positive influence on society and benefit humans, assist in complex environments, automate processes and push the frontier towards collective intelligence. Our focus lies on the development of AI approaches inspired by biological and feedback learning, hence the name “cybernetics”. David A. Mindell defined cybernetics as “the study of human/machine interaction”, whereby he states the core idea that most any system can be analyzed using the principles of feedback, communication and control, which we emulate in our research. Developing holistic approaches to intricate problems requires the usage of more than one tool, which is why we leverage the best suited approaches, ranging from multi-agent, distributed and hierarchical to causal RL. Our AI-agents should sense and interact with both their environment and other human and artificial agents within it, while behaving in a predictable and reliable manner. Our fields of applicability are virtually unlimited; integrating AI into transport systems such as aviation and rail, supporting human decision-makers, fully automating processes and robotics, to name a few. It is essential to us to maintain human agency and control over systems, ensuring that human domain knowledge and expertise is not lost or replaced, but supported and enhanced.
Also see: https://cyberneticlearningsystems.github.io/
Services
- Insight: keynotes, trainings, public outreach, scientific diplomacy
- AI consultancy: workshops, expert support, advise, technology and ethical assessment
- Research and development: small to large-scale collaborative projects, third party-funded research, student projects, commercially applicable prototypes
Team
Projects
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Deep Dive ML on Simulated Enzyme-Electrolysis Performance
The goal of this pilot study is to research requirements needed to develop a computational model that simulates the fluidic and electro-biochemical dynamics in the power-to-liquid process in order to optimise the performance, efficiency and longevity of enzymes.
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AI4REALNET: AI for REAL-world NETwork operation
The scope of AI4REALNET covers the perspective of AI-based solutions addressing critical systems (electricity, railway, and air traffic management) modelled by networks that can be simulated, and are traditionally operated by humans, and where AI systems complement and augment human abilities. It has two main ...
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FarmAI – Artificial intelligence for Farming Simulator
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Welle 7
The Migros Aare Bern is realizing a highly frequented central location with various types of businesses at the Bern railway station. On an area of 10,000 square meters, an experience, education and shopping center is being designed in the immediate vicinity of the train station (called Wave). Located in the western ...
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Developing new behavioural models for simulating pedestrian flows and an optimized revenue concept
The simulation of pedestrian flows has become of increasing importance and attention over the last years. With the resulting innovations this project aims to improve the quality of the simulation results substantially. Furthermore the duration of simulation projects shall be reduced to improve competitiveness. This ...
Publications
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Renold, Manuel; Vollenweider, Janik; Mijović, Nemanja; Kuljanin, Jovana; Kalić, Milica,
2023.
Journal of Air Transport Management.
106(102305).
Available from: https://doi.org/10.1016/j.jairtraman.2022.102305
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Schlögl, Sebastian; Bader, Nico; Anet, Julien Gérard; Frey, Martin; Spirig, Curdin; Renold, Manuel; Gutbrod, Karl,
2021.
In:
EGU General Assembly 2021, Online, 19-30 April 2021.
European Geosciences Union.
Available from: https://doi.org/10.5194/egusphere-egu21-14143
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Anet, Julien G.; Schlögl, Sebastian; Spirig, Curdin; Frey, Martin P.; Renold, Manuel; Gutbrod, Karl G.,
2021.
Building a new high-density air temperature measurement network in two Swiss cities [poster].
In:
EGU General Assembly 2021, Online, 19-30 April 2021.
European Geosciences Union.
Available from: https://doi.org/10.5194/egusphere-egu21-9102
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Kuljanin, Jovana; Kalić, Milica; Begović, Boris; Mijović, Nemanja; Renold, Manuel,
2021.
Journal of Air Transport Management.
91(102016).
Available from: https://doi.org/10.1016/j.jairtraman.2020.102016
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Renold, Manuel; Kuljanin, Jovana; Kalić, Milica,
2019.
In:
Kazda, Antonin; Smojver, Ivica, eds.,
INAIR 2019 : Global Trends in Aviation.
8th International Conference on Air Transport (INAIR), Budapest, Hungary, 12-13 November 2019.
Elsevier.
pp. 178-187.
Transportation Research Procedia.
Available from: https://doi.org/10.1016/j.trpro.2019.12.032