If you’re a video gamer, you’ve likely experienced an emotional peak or two while you’ve been playing. From the euphoric high that comes with completing a difficult level, to the sheer rage that overcomes many a Mario Kart player as they’re hit by a blue shell just before the finish line, emotions are an important part of gaming. Often, your emotional reaction is linked to a game’s difficulty – if a game is too hard, it’s likely to be annoying or infuriating, whereas an overly easy game is boring. You want to be in that sweet spot to maximise player satisfaction, and a Korean research team may have found the answer.
In a recent study published in Expert Systems With Applications, scientists at the Gwangju Institute of Science and Technology (GIST) developed a novel approach for appropriately balancing game difficulty, using machine learning to optimise the AI challenge based on your emotional state while playing. If the approach works, it should theoretically contribute to the balancing of game difficulty, while subsequently making the games more fun and appealing to a broader selection of players.
The team took the concept of dynamic difficulty adjustment (DDA) as their starting point. DDA is a method of automatically modifying a game’s features and scenarios in real time, depending on the player’s skill. Or, as EA described it, a “technique for adaptively changing a game to make it easier or harder”. This may be incredibly overt – a game might suggest that you change to a lower difficulty if you’re having trouble. Or it may be more subtly applied – providing you with more power-ups or some earlier checkpoints if you’re struggling, or increasing the enemy health bar if you’re doing very well.
“They used an algorithm […] to determine an AI player’s next move and ensure it was one that improved the human player’s affective state“
Use machine learning to optimize the challenge based on the player’s emotional state while playing the game! This sounds amazing. The historical contribution of artificial intelligence to optimizing the gaming experience is undeniable, but machine learning through “emotions” sounds new to me.
According to the author’s description, the artificial intelligence may suggest that players change to a lower difficulty if they are having struggles. Or it might be applied more subtly – providing the player with more energy boosts or some earlier checkpoints if the player is struggling (which sounds effective), or increasing enemy life points if the player is doing well (which sounds need to reconsiderate a bit).
This idea sounds awesome, but I think some points need to reconsider. Again, back to the point, the AI would increase enemy life points if the player is doing well, which I think this might not working great. Depending on the psychology of different users, AI should treat the players differently. For example, some players prefer a “grass-cutting experience”, which means solving problems and kill enemies quickly. In this case, the target users of the adaptive difficulty proposed by the authors are obviously not them.
Goodall, R. (2022, October 15). Scientists develop model to control video game difficulty via emotion. The Boar. Retrieved January 30, 2023, from https://theboar.org/2022/10/scientists-develop-model-to-control-video-game-difficulty-via-emotion/