AI for Gameplay

Description: What are the challenges in writing an AI that can generate an interesting settlement adapted to an unseen Minecraft map? What AI techniques work well here? These are just some of the questions the GDMC AI settlement generation challenge set out to answer when it was founded 7 years ago. Adaptive and holistic procedural content generation in games still has many open challenges, and in this talk we will take a look at some selected ideas and AI techniques used to tackle those. I will show a range of colorful and amazing settlement generators, and discuss what worked and what did not. If you feel after the talk that you could do better, I have great news for you, we will be back in 2025 for round 8.

Takeaways:

• Learning about the GDMC competition – a fun AI programming competition.
• The use of AI competition to drive research and find out things.
• A comparison of different AI approaches to the same problem.
• A better understanding of the problems of adaptive PCG.

Description: A continuation and recontextualization of my 2024 GDC AI Summit talk “How ‘Ara: History Untold’ Transformed AI in 4X Games”, I will summarize Ara: History Untold’s novel system, and dig into details around optimizing its performance, and usability in the final months leading up to ship.

Takeaways:

• An introduction to semi-obscure but very useful AI topics (HGNs, Logical DSLs)
• Insights into making performant data structures for complex game AI
• Practical examples of how to make trades between effectiveness and complexity

Description: Computing the position of objects in a game is one of the most frequent features to be carried out: where to stop a movement (to avoid a collision)? Where to hide, protect or start a jump? Where do two zones intersect (which objects will be affected by the zone of effect of my action)?

The goal of this presentation is to suggest that solving the analytical geometry equations resulting from the various game situations is preferable to using the various environment projection queries provided by your favourite game engine: first write the equations to which the coordinates of the game objects must conform, then solve these equations to obtain the numerical values of these coordinates; and don’t forget to get help from a math software. These two steps will be illustrated in the case of projecting a grid onto a navigation surface for the Unreal Engine with the help of Mathematica.

Takeaways: This presentation is intended for AI game developers facing one of the three situations:

• Too much computing resources are spent generating sample locations and then filtering them
  • Write and solve the equations modelling the geometric problem
• Too many erroneous or approximate locations require post-processing to be usable
  • Compute values from exact solutions of your geometric problems
• Bullet proofs tests of locations are needed
  • Compare locations with exact solutions of your geometric equations