International Symposium on Agentic and Trustworthy Learning for Autonomous Systems

Scope

ATLAS Symposium invites theoretical, methodological, and applied contributions across agentic and trustworthy learning for autonomous systems, and welcomes work at any stage of maturity. On the methods side, relevant topics include agentic AI and autonomous multi-agent decision-making; foundation models and generative AI for autonomous applications; reinforcement, continual, self-supervised, and federated learning; multimodal perception, reasoning, and planning; embodied intelligence; human-agent collaboration; and distributed edge intelligence. Trustworthiness themes span safety assurance and verification, reliability, explainability, uncertainty quantification, risk aware learning, adversarial robustness, privacy, cybersecurity, accountability, and alignment with human values. Application domains include autonomous vehicles and intelligent transportation, robots and drones, IoT platforms, cyber-physical systems, smart infrastructure, and 6G-enabled services. Participants are further encouraged to address resource-efficient deployment, communication-efficient federation, digital twins, evaluation frameworks, datasets, simulation environments and testbeds, standards, governance, and the ethical and societal implications of increasingly autonomous AI.