Teaching
Courses, lecture material and educational resources.
Artificial Intelligence is one of the few disciplines where teaching and research evolve at an exceptional pace. The material collected on this page brings together a selection of postgraduate courses that I have designed and delivered over the past decade, covering topics ranging from Game AI and Statistical Planning to modern Game Development and AI-assisted Game Design. Each course combines theoretical foundations with practical exercises based on research software and benchmarks developed within my research, providing students with hands-on experience using the same tools and methodologies employed in contemporary Game AI research.
Statistical Planning and Reinforcement Learning (MSc)
A postgraduate course on statistical planning methods for Game AI, combining lectures with practical work in TAG and Pommerman. The material covers core ideas in forward planning and agent design, with an emphasis on implementing and evaluating AI methods in game environments.
Resources
- 📄 Lecture Slides: Lecture decks
- 💻 Laboratory Sessions: Practical exercises
- 📝 Coursework: Assignments and marking criteria
- 🎥 Video Lectures (2022): YouTube playlist
Related project: TAG — Tabletop Games Framework
Advanced Game Development (MSc)
This Master’s course focuses on the software engineering principles behind modern game engines and large-scale game development. Through a combination of lectures and practical programming exercises, students learn how to design efficient, modular and maintainable game architectures, covering topics such as game loops, object management, entity-component systems, memory management and event-driven programming.
The course places a strong emphasis on professional development practices, with laboratory sessions and coursework guiding students through the implementation of increasingly sophisticated game engine components in C++.
Resources
- 📄 Lecture Slides: Lecture material
- 💻 Laboratory Sessions: Programming exercises and model solutions
- 📘 C++ Tutorial: Comprehensive reference slides
AI for Game Design (MSc / PhD)
This postgraduate course explores how Artificial Intelligence can support and automate the game design process. Using the TAG framework as the main experimental platform, students investigate AI agents, game spaces and metrics, automated playtesting, game parameter optimisation and quantitative methods for analysing and improving game designs. The course combines lectures with practical programming exercises that expose students to current research topics in AI-assisted game design.
Resources
- 📄 Lecture Slides: Lecture material
- 💻 Programming Exercises: Hands-on exercises using TAG
- 📚 Extra Resources: Additional examples and research material
Related project: TAG — Tabletop Games Framework
Game AI Seminar Series (MSc / PhD)
A four-part seminar series introducing postgraduate students to the breadth of modern Game AI research. The series provides an overview of the field before exploring Statistical Forward Planning, General Video Game Playing and AI for Game Design, highlighting both the underlying algorithms and current research challenges. The seminars are intended as an entry point for students interested in pursuing research in Game AI.
Resources
- 📄 Seminar Slides: Four-days seminar lectures