Explores applications of autonomous agents in UAVs, air traffic management, and logistics, focusing on MAS interactions and adaptive transportation networks.
Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.
Explores the challenges and distinctions between human and artificial autonomy, touching on ethical implications and the conditions required for true autonomy.
Covers the characteristics, applications, and challenges of intelligent agents in software systems, emphasizing their role in making autonomous decisions and coordinating with other agents.
Delves into spatial memory usage in RL agents for maze navigation tasks, showing improved performance with visual landmarks but inconsistent results in path choosing.
Covers the Model-View-Controller architecture for interactive graphical programs, emphasizing separation of concerns and the roles of model, view, and controller.
