SS06 - Generative System Design for Autonomous Systems

Special Session Organized by

Shahram Eivazi, Festo, Germany, and Jan Seyler, Festo, Germany,

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In an era where engineering complexity is escalating, the traditional methodologies for system design, especially in autonomous systems, are facing significant challenges. The advent of generative system design introduces a paradigm shift, promising to revolutionize the way we approach engineering tasks and processes. This Special Session on Generative System Design for Autonomous Systems at the forthcoming ETFA conference seeks to explore the frontier of generative methodologies in engineering. With an emphasis on generative AI, including evolutionary algorithms, generative networks, reinforcement learning, and large language models, this session is poised to showcase cutting-edge methodologies, insightful case studies, and the latest trends in the field. From conceptual designs to system-level integrations and component optimizations, the session will cover an extensive range of topics, emphasizing the transformative impact of generative system design in autonomous systems. We invite submissions that focus on, but are not limited to, the following topics:

Topics under this session include (but not limited to)

  • Modelling of Automation Tasks and Processes: Innovative approaches to representing and understanding complex automation tasks and processes within autonomous systems.
  • Simulation Techniques for Data Generation or Process Optimization: Advanced simulation methodologies that contribute to efficient data generation, system testing, or process optimization in autonomous systems.
  • Large Language Models for System Synthesis: Exploration of how large language models can be leveraged for the synthesis and integration of complex autonomous systems.
  • Neural Networks and Reinforcement Learning for Engineering Design: Cutting-edge applications of neural networks and reinforcement learning in the context of engineering design, focusing on autonomous systems.
  • Design, Topology, and Process Optimization: Novel strategies for optimizing the design, topology, and operational processes of autonomous systems, ensuring efficiency and adaptability.
  • Advanced Motion and Task Planning in Autonomous Systems: This topic will explore innovative strategies and algorithms in motion planning and task execution within autonomous systems, emphasizing efficiency, safety, and adaptability in dynamic environments.
  • Integrating Symbolic and Subsymbolic AI for Enhanced System Intelligence: This area will focus on the synergy between symbolic AI, with its rule-based processing and logical reasoning, and subsymbolic AI, such as neural networks, to create more robust and intelligent autonomous systems through knowledge-guided machine learning.