A systems-driven stealth experience focused on AI perception, player visibility, and dynamic alert state transitions. Designed with modular architecture to allow scalable stealth behaviors and performance-conscious gameplay systems.
Night at the Museum is a first-person stealth prototype centered around systemic AI detection and tension-driven gameplay. The core objective was to build a scalable stealth framework that could support patrol systems, alert escalation, and player feedback loops.
One of the primary challenges was ensuring smooth transitions between AI states without creating erratic behavior. This was solved by implementing controlled state machine logic and clearly defined transition conditions.
Another focus area was performance optimization. AI logic was structured to minimize unnecessary runtime checks, ensuring stable performance with multiple active enemies.