Robust Artificial Intelligence
Summary
Aim
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. The focus of this session is to investigate the diverse meaning of robust AI and gathers 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:
Knowledge-driven models
Reasoning-based methods
Robustness analysis
Trustworthiness
Machine learning biases
Adversarial attacks and security
Cognitive models and bio-inspired AI
Hybrid-models
Explainable AI
Call for Papers
IMPORTANT DATES
Paper submission deadline: 15th May, 2023
Notification of acceptance: 22th May, 2023
Camera ready: 29th May, 2023
Invited session at the internal KES conference, Athens, Greece, 6-8 September 2023
Authors who submit and present their work will have their work published and indexed internationally by Elsevier's Procedia Computer Science.
Accepted Papers
A Web-Based Platform for Efficient and Robust Simulation of Aquaculture Systems using Integrated Intelligent Agents. Dr. Aya Saad, Dr. Biao Su, Dr. Finn Olav Bjørnson.
Nonlinear 𝐻∞ Control Via Min-Max Adaptive Dynamic Programming for Unknown Dynamics. Phd Student Mohammad Sarbaz, Dr. Wei Sun, Dr. Theodore Trafalis.
Towards Robustness Analysis for Adaptive Artificial Intelligence in Multi-Autonomous agent systems. Professor Anne Håkansson, Phd Student Yigit Can Dündar, Professor Ronald Hartung
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