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_posts/projects/2020-05-01-FIKS.md
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@ -24,12 +24,14 @@ skills: Multi-Agent Reinforcement Learning (MARL), Emergence Analysis, AI Safety
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![Robot Arm Icon](/assets/images/projects/robot.png){: .align-left style="padding:0.1em; width:5em"}
In collaboration with Fraunhofer IKS, the AI-Fusion project addressed the critical challenge of understanding and ensuring safety in multi-agent reinforcement learning (MARL) systems. Emergence, defined as the arising of complex, often unpredictable, system-level dynamics from local interactions between agents and their environment, was a central focus due to its implications for system safety and reliability.
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**Project:** AI-Fusion<br>
**Partner:** [Fraunhofer Institute for Cognitive Systems (IKS)](https://www.iks.fraunhofer.de/)<br>
**Duration:** 2022 - 2023<br>
**Objective:** To investigate the detection and mitigation of potentially unsafe emergent behaviors in complex systems composed of multiple interacting AI agents, particularly in scenarios involving heterogeneous agents (e.g., mixed-vendor autonomous systems).
In collaboration with Fraunhofer IKS, the AI-Fusion project addressed the critical challenge of understanding and ensuring safety in multi-agent reinforcement learning (MARL) systems. Emergence, defined as the arising of complex, often unpredictable, system-level dynamics from local interactions between agents and their environment, was a central focus due to its implications for system safety and reliability.
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