The research focus of Learning Agents and Robots MRG is on reinforcement learning, deep learning, optimization, control, multiagent systems and computational intelligence methods for creating autonomous software and robotic agents that learn to solve tasks and adapt to dynamic environments.
A key goal of artificial intelligence research is to create smart systems that sense and understand the world and learn to make optimal decisions over time based on feedback, which can be provided either by humans or other processes. This research agenda lies at the intersection of computer science, mathematics, engineering, economics, neuroscience and psychology, and has already produced a wide range of applications in a variety of domains such as games, robotics, smart grids, autonomous cars, industrial automation, dialogue and tutoring systems, recommendation systems, advertising, healthcare and finance.
The Learning Agents and Robots (LEAR) MRG aims to conduct basic and applied research on innovative methodologies and algorithms for creating such intelligent agents that can (1) interact with humans and learn from them, (2) act autonomously by learning to solve tasks and (3) become highly adaptive to dynamic environmental changes. Emphasis will be placed on applying these methodologies in games, simulations and physical robots.
MRG leader:
Dr. Vassilis Vassiliades