It’s right behind you, breathing down your neck, seemingly aware of your every move. You turn left, then right, weaving between pillars to collect precious orbs while dodging its advance. For a moment, it seems you’ve lost it. But then your foe appears around a corner, and bam! It has you.
The familiar Game Over screen appears—but then, just below, something different. “Please report difficulty level.” You lasted just a few seconds in this hair-raising chase, so, adrenaline still pumping, you tap “Hard.” Next time, the thing stalking your every step will be a tad less aggressive.
Artificial Intelligence Joins the Battle!Romain Trachel and Alexandre Peyrot, machine-learning specialists at Eidos-Sherbrooke, demonstrated the game I just described at Unreal Fest 2022. It combines machine learning with an Unreal Engine feature called the Environment Query System (EQS), which lets developers use spatial data to inform AI decisions.
Normally, this is handled through behavior trees that layer variables and branching possibilities. But in this demo, the AI behavior is driven by a machine-learning model. Unreal EQS acts as the AI’s eyes and ears, providing information about its environment, while the machine-learning model becomes its brain and decides how it should respond.
The game is not as frightful as I made it sound, mostly because of its top-down presentation and placeholder visuals, but its gameplay is a classic cat-and-mouse chase that tasks players with collecting orbs strewn across a map. It’s Pac-Man, basically—but the ghost’s behaviors are no longer scripted.
“So, for instance, if a developer decided to activate a stronger chase mode, the only thing to do is to increase a reference value in the EQS tests,” Trachel and Peyrot say in an email. “It really has the potential to simplify the development workflow, because in actual game productions, it would be up to a game designer to decide which game variables must be tuned in order to change the difficulty.”
