Ian Gulland Lecture Theatre

About the talk
We will explore the relation of design and iteration on the example of NPC perception in Kingdom Come: Deliverance and its sequel. How we made NPCs see in a realistic, open world RPG, what we thought is going to work and what we actually had to do to make it work. What it takes to tune a system to behave in both scripted and emergent situations, to provide enjoyable but nontrivial gameplay and enhance immersion. All that under 3 ms per frame. We will be reminded of lessons that might seem basic but still could pose a challenge over 10+ years of development.

Takeaway
– Designing for everything is doomed to fail; why?
– Iteration takes long but can lead to success
– The importance of stable teams
– The importance of selling features to the player

Experience level needed: Intermediate

About the talk
I’m not a game designer; I am an AI Product Manager with a psychology background who recently transitioned into the gaming industry. But I’ve spent the past several years building the AI companions Replika and Blush, which millions of people talk to every day. I’ve seen firsthand how and why people form emotional bonds with AI. In this talk, I’ll explore what the phenomenon of AI companionship can teach us about designing richer, more immersive experiences in games.
We’ll look at how advances in generative AI might enable NPCs to become interactive companions. This talk will analyze the design/UX strategies and psychological principles that make AI “friends” engaging. We will explore why people want to talk to an AI and form these connections. We’ll also dive into the ethical and safety implications of AI companions, from safeguarding users (especially younger players) against harm to preventing emotional over-dependence, and discuss best practices for responsible implementation. Hopefully, by the end of this talk, the audience will gain a nuanced understanding of AI companionship that goes beyond simplistic narratives of benefit or harm.

Takeaway
– Learn from real – world successes and failures.The audience will see what actually works and what doesn’t when creating AI characters based on lessons from AI companions. We’ll explore strategies for creating believable AI characters and learn why defining an AI’s personality and boundaries – before – you start is critical.
– Understand the psychology of player attachment. This talk will explore – why – players get attached to AI, helping to understand the human needs these companions meet like combating loneliness or practicing social skills.
– Start thinking about building AI companion experiences ethically and safely. Audience will get an idea of the real emotional implications for players and responsible design in the realm of AI Companions.

Experience level needed: Beginner

About the talk
Reinforcement learning promises powerful game AI, but making it production-ready is another story. This talk shares Riot’s lessons from working at the intersection of research and live games—where safety, evaluation, and reliability matter as much as raw performance. We’ll highlight common failure modes, suggest ways to manage variance and telemetry, and offer ideas for how RL can be applied responsibly in production. Along the way, we’ll reflect on what’s realistic today versus what remains hype, and how to get real value out of RL without overpromising.

About the session
Investment into AI companies is sky-high while investment into games companies has fallen from their recent Covid peak. So when AI and games intersects, where does the smart money go? In this panel, leading investors share insights into how they evaluate opportunities in this space. From gameplay powered by machine learning to AI-assisted tools for game development, learn about what’s hot, what’s risky, and what’s next.

Takeaway
– Knowledge on how different types of investors evaluate opportunities in games and AI
– Insights into what are the hottest areas for investments in the space
– Tips for founders and developers looking to attract investment
– Understanding of what investors perceive to be the greatest risks
– Perspectives on the future of AI with games

Experience level needed: Beginner

About the talk
Debugging complex algorithms can be difficult, especially for complex AI behaviours which can be executed over several frames or asynchronously is a nightmare. But doing this across all major gaming platforms? That’s a unique horror for AAA game developers. At Havok we have fought this battle for years, and we’re here to introduce you to our weapon of choice: Cross-Platform Determinism. Determinism allows us to relive crash scenarios as often as we want across platforms. A bug found on a console can be replayed on PC in debug. Saving time, reducing frustration, and simplifying debugging. Saying development is now a dream might be pushing it a bit too far, but only a bit!

In this talk you will learn how determinism, and in particular cross-platform determinism, can help game developers implement better tooling for recording player sessions and reproducing issues. From running fast math operations on different CPU architectures, to managing deterministic multi-threading models, and dealing with compiler bugs, learn practical tips to implement cross-platform determinism in your game.

About this Talk
Dungeon Keeper was once called “Game of the Millennium” – whether or not that’s true, Bullfrog Production’s RTS remains an early use of ‘real AI’ in games. Evil minions ran around with high autonomy in a user-generated Dungeon to build emergent gameplay with dark humour. This is the story of how that was done. Retro-AI architecture seeks to inform character behaviours today, with an emphasis on how to build character navigation, motion and spatial perception against constantly changing design directions.

Takeaway
– How to evolve an AI system before you know what the game is…
– …and to tune the AI system against (insane) design and hardware constraints.
– Why AI architecture needs compact data, clear representations, layered functionality, and chains-of-tools.
VALUE: character motion is still a top priority today. What was real-time a couple of decades ago is feasible today: per-character; inside dynamic tool-chains; at search-time; at inference time.