The focus of this survey is on research in applying evolutionary and other metaheuristic
search algorithms to automatically generating content for games, both digital and nondigital …

Procedural content generation (PCG) refers to the practice of generating game content, such
as levels, quests or characters, algorithmically. Motivated by the need to make games …

Training generally-capable agents with reinforcement learning (RL) remains a significant
challenge. A promising avenue for improving the robustness of RL agents is through the use …

Foundation models have shown impressive adaptation and scalability in supervised and self-
supervised learning problems, but so far these successes have not fully translated to …

Foundation models have shown impressive adaptation and scalability in supervised and self-
supervised learning problems, but so far these successes have not fully translated to …

Georgios N. Yannakakis Julian Togelius Page 1 Artificial Intelligence and Games Georgios N.
Yannakakis Julian Togelius Page 2 Artificial Intelligence and Games Page 3 Georgios N …

Automatically generating novel and interesting games is a complex task. Challenges include
representing game rules in a computationally workable form, searching through the large …

Hundreds of millions of people play computer games every day. For them, game content—
from 3D objects to abstract puzzles—plays a major entertainment role. Manual labor has so …

Procedural content generation (PCG) is an increasingly important area of technology within
modern human-computer interaction (HCI) design. Personalization of user experience via …

In many reinforcement learning tasks, the goal is to learn a policy to manipulate an agent,
whose design is fixed, to maximize some notion of cumulative reward. The design of the …