Projects

Our research project explores the application of AI and machine learning techniques in video games, focusing on procedural content generation and AI-driven advice. We aim to dynamically generate diverse game content and create intelligent virtual companions that offer personalised guidance and strategies to enhance player experiences.

In this line of work, we are interested in techniques that allow an agent to leverage past knowledge to solve new tasks quickly. In particular, we focus on how agents can acquire behaviours that can be combined to generate interesting, novel abilities.

Our group competes in the RoboCupSoccer 3D Simulation, in which a team of 11 simulated Nau robots compete in a game of football against other teams from around the world. Our focus here is both on improving the low-level control of individual robots and incorporating high-level, multi-agent decision making into the team’s strategy.

Here we focus on learning abstract representations, which we believe are an important component if we are ever to apply reinforcement learning to the real world. In particular, we focus on skill- and symbol-discovery, as well as the interplay between the two.