AI for Games in the Foundation Model Era
Survey organizes foundation-model AI for games into six roles and analyzes which capabilities transfer across playing, design, building, runtime adaptation, and testing.
A survey maps foundation-model and learned world-model research across the game lifecycle into six roles: playing/acting, modeling players and games, designing games, building/maintaining games, runtime generation/adaptation, and testing/evaluation. The authors identify cross-role connections such as trajectories training world models and design specifications driving executable implementations. Control schemes, rules, engine interfaces, state representations, and player contexts often remain setting-specific, so downstream claims require validation in the target setting. Evaluation is most standardized for bounded game playing, while persistent state, repeated revision, validated player modeling, and automated testing remain less established.
- Organizes game AI literature into six roles by immediate use of AI output
- Identifies cross-role connections: trajectories train world models, designs drive implementations
- Control schemes, engine interfaces, and player contexts remain setting-specific
- Evaluation weakest for persistent state, player modeling, runtime adaptation
From the original article
1 min read at huggingface.co
Foundation models, alongside advances in learned game-world models, are reshaping AI across the game lifecycle. Beyond playing games, recent systems model players and game dynamics, support design and development, adapt player-facing experiences at runtime, and evaluate resulting artifacts.
By Meng Luo, Yanlin Li, Hao Li · huggingface.co
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