This research paper identifies an accountability deficit for autonomous AI agents operating in smart city critical infrastructure under the EU AI Act, noting that specific provisions exclude safety-component AI from certain explanation rights and impact assessments. It proposes AgentGov-SC, a three-layer governance architecture specifying 25 measures, 5 conflict resolution rules, and an autonomy-calibrated activation model, with bidirectional traceability to established AI frameworks. A scenario analysis traces the governance activation through a multi-agent corridor cascade involving documented UAE smart-city systems. Why it matters: This paper addresses a significant regulatory gap in AI governance for complex, multi-agent systems in critical urban infrastructure, offering a novel architectural solution highly relevant to global smart city initiatives, including those in the Middle East.
This paper introduces a decentralized multi-agent decision-making framework for search and action problems under time constraints, treating time as a budgeted resource where actions have costs and rewards. The approach uses probabilistic reasoning to optimize decisions, maximizing reward within the given time. Evaluated in a simulated search, pick, and place scenario inspired by the Mohamed Bin Zayed International Robotics Challenge (MBZIRC), the algorithm outperformed benchmark strategies. Why it matters: The framework's validation in a Gazebo environment signals potential for real-world robotic applications, particularly in time-sensitive and cooperative tasks within the robotics domain in the UAE.
Dr. Yali Du from King's College London will give a presentation on learning to cooperate in multi-agent systems. Her research focuses on enabling cooperative and responsible behavior in machines using reinforcement learning and foundation models. She will discuss enhancing collaboration within social contexts, fostering human-AI coordination, and achieving scalable alignment. Why it matters: This highlights the growing importance of research into multi-agent systems and human-AI interaction, crucial for developing AI that integrates effectively and ethically into society.