Robust, Adaptive and Generative AI

Summary

Aim and scope

Methods in AI for robotic control, mobile platforms, and cognitive cyber-physical systems are developing rapidly. They tackle the challenging task of modeling real-world systems and environments through data, using machine vision, reinforcement learning for control, probabilistic machine learning, among many others. Such data-driven approaches have led to many concerns regarding the robustness, stability, and overall safety of these systems.

While data-driven approaches based on learning algorithms have seen huge success in the last decade, when applied to cyber-physical systems such as manufacturing applications and healthcare robotics, the lack of safety guarantees causes trust issues. A central challenge is defining and implementing robustness for different applications and providing methods for analyzing and verifying models. In this context, the session emphasizes the critical necessity for robust decision-making processes to ensure the reliability and safety of AI applications. The focus of this session is to investigate the diverse meaning of robust AI and gather a wide array of approaches to the problem. 

The proposed invited session provides a forum for bringing together researchers from academia and industry to explore and present their findings in Robust Artificial Intelligence with theories, systems, technologies, and approaches for testing and validating them on challenging real-world, safety-critical applications.

Topics 

Research papers on all aspects of Robust AI. Topics include, but are not limited to: 


Call for Papers

IMPORTANT DATES

Invited session at the internal KES conference, Seville, Spain, 6-8 September 2024

Authors who submit and present their work will have their work published and indexed internationally by Elsevier's Procedia Computer Science.

Accepted Papers

Organizers

Aya Saad, Research Scientist, aya.saad@sintef.no , SINTEF Ocean, Trondheim, Norway

Anne Håkansson, Professor, anne.hakansson@uit.no, UiT Norges arktiske universitet, Postboks 6050 Langnes, 9037 Tromsø, Norway

Email & Contact Details

Aya Saad, Research Scientist, aya.saad@sintef.no, SINTEF Ocean, Trondheim, Norway

Professor Anne Håkansson, anne.hakansson@uit.no, IFI, UIT, Tromsö, Norway