Puzzle Game
Puzzle games serve as valuable benchmarks for evaluating artificial intelligence, particularly the reasoning and strategic capabilities of large language models (LLMs) and reinforcement learning (RL) agents. Current research focuses on developing robust evaluation datasets and metrics, exploring the effectiveness of various model architectures like diffusion models and transformers for solving different puzzle types (e.g., grid-based, jigsaw, spatial), and investigating methods for procedurally generating puzzles and adapting difficulty levels. These studies contribute to a deeper understanding of AI's problem-solving abilities and inform the development of more sophisticated algorithms for complex decision-making tasks in robotics and other domains.
Papers
Estimating player completion rate in mobile puzzle games using reinforcement learning
Jeppe Theiss Kristensen, Arturo Valdivia, Paolo Burelli
Procedural content generation of puzzle games using conditional generative adversarial networks
Andreas Hald, Jens Struckmann Hansen, Jeppe Kristensen, Paolo Burelli