Runtime Generation and Adaptation with Foundation Models

How does generative AI adapt games at runtime?

Generative AI for games at runtime changes content or behavior while a player is actively playing. Systems can create dialogue, speech, quests, props, characters, and narrative events in response to current actions and world state. They can also adapt difficulty, pacing, recommendations, companion behavior, or story direction using player models and session history.

Runtime systems face constraints that offline design tools can often avoid. An output must arrive within a useful latency budget, fit the active rules, respect prior events, remain safe, and avoid leaving the game in an invalid state. Long-term memory raises questions about what characters know, what the world records, and whether players and agents share a consistent history. Voice interaction also adds recognition, synthesis, turn-taking, interruption, and performance costs.

Personalization should be compared with a non-adaptive baseline and should distinguish temporary session context from stable player preferences. Useful evidence includes response latency, content validity, state agreement, controllability, recovery from model failures, and measured effects on player outcomes across repeated sessions. A fluent response is only a proposal until the engine validates and applies the corresponding game action.

This topic is surveyed in AI for Games in the Foundation Model Era.