A team of researchers from Gwangju Institute of Science and Technology, South Korea, has come up with a unique approach that they said can “adjust videogame difficulty based on player emotions during gameplay rather than on the player's performance", in order to provide a better ‘gaming experience’.
Generally, balancing a videogame’s difficulty is essential to provide players with a pleasant experience, the scientists said. However, ‘difficulty’ is a tough aspect to balance in video games, as ‘easy’ or ‘difficult’ are subjective terms. To make this process easier, most developers use ‘dynamic difficulty adjustment (DDA), a technique to adjust the difficulty of a game in real time according to player performance.
For example, if player performance exceeds the developer’s expectations for a given difficulty level, the game’s DDA agent can automatically raise the difficulty to increase the challenge presented to the player.
“Though useful, this strategy is limited in that only player performance is taken into account, not how much fun they are actually having,” said the researchers, who decided to put a twist on the DDA approach.
Therefore, instead of focusing on the player’s performance, they developed DDA agents that adjusted the game’s difficulty to maximise one of four different aspects related to a player’s satisfaction, including challenge, competence, flow, and valence. The DDA agents were trained via machine learning (ML) using data gathered from actual human players, who played a fighting game against various artificial intelligences (AIs) and then answered a questionnaire about their experience.
“One advantage of our approach over other emotion-centred methods is that it does not rely on external sensors, such as electroencephalography, a technique for recording and interpreting the electrical activity of the brain," said associate professor Kyung-Joong Kim, who led the study. He added that this technique can have significant potential for other fields that can be ‘gamified,’ such as healthcare, exercise, education and more.
The team verified through an experiment with 20 volunteers that the proposed DDA agents could produce AIs that improved the players’ overall experience, no matter their preference. "This marks the first time that affective states are incorporated directly into DDA agents, which could be useful for commercial games," said Kim.
"Commercial game companies already have huge amounts of player data. They can exploit these data to model the players and solve various issues related to game balancing using our approach,” he added.