The ultimate aim of my research is to build explainable sequential decision-making systems.

By explainable I mean human-centric AI agents that are understood by their users, that understand their users, and that are effective and trustworthy collaborators for their users. By sequential decision-making I mean tasks that extend over time, and require an autonomous agent to make many smart choices to achieve some user goal.

To achieve this, my research focuses on planning, required for acting towards long-term goals; on learning, required for acting in unknown environments; and on the explainability of planning and learning, required for successful human-AI collaboration.

In order to tackle these challenges, I often use a variety of traditional and digital games as testbeds. I then transfer game AI technology to non-game domains in collaborative projects with industry, in sectors such as logistics and transportation, smart manufacturing, and sustainable energy.