6 papers
Evolutionary Wave Function Collapse
Dipika Rajesh, Ahmed Khalifa, Julian Togelius
Wave Function Collapse (WFC) is a widely used procedural content generation method that learns local adjacency constraints from example inputs to generate larger outputs. In this p…
Learning Local Constraints for Reinforcement-Learned Content Generators
Debosmita Bhaumik, Julian Togelius, Georgios N. Yannakakis +1
Constraint-based game content generators that learn local constraints from existing content, such as Wave Function Collapse (WFC), can generate visually satisfying game levels but…
Evolutionary Level Repair
Debosmita Bhaumik, Julian Togelius, Georgios N. Yannakakis +1
We address the problem of game level repair, which consists of taking a designed but non-functional game level and making it functional. This might consist of ensuring the complete…
The Procedural Content Generation Benchmark: An Open-source Testbed for Generative Challenges in Games
Ahmed Khalifa, Roberto Gallotta, Matthew Barthet +3
This paper introduces the Procedural Content Generation Benchmark for evaluating generative algorithms on different game content creation tasks. The benchmark comes with 12 game-re…
Affectively Framework: Towards Human-like Affect-Based Agents
Matthew Barthet, Roberto Gallotta, Ahmed Khalifa +2
Game environments offer a unique opportunity for training virtual agents due to their interactive nature, which provides diverse play traces and affect labels. Despite their potent…
Closing the Affective Loop via Experience-Driven Reinforcement Learning Designers
Matthew Barthet, Diogo Branco, Roberto Gallotta +2
Autonomously tailoring content to a set of predetermined affective patterns has long been considered the holy grail of affect-aware human-computer interaction at large. The experie…