Automated Playtesting with a Cognitive Model of Sensorimotor Coordination

Abstract

Playtesting is widely performed in the game industry to gauge the difficulty of a game. A large number of test participants with different skills must be recruited for reliable test results, resulting in high costs. Automated playtesting based on player simulation is expected to reduce playtesting costs. Still, it has not yet been widely applied due to the lack of a method that realistically simulates players gameplays with different skills. Based on a cognitive model of sensorimotor coordination that explains the human button input process, we propose a novel automated playtesting technique that predicts the game difficulty experienced by players with different skills in moving-target acquisition (MTA) games. The model has free parameters representing the inherent skills of players. Once the parameters are obtained for a specific population (e.g., seniors), it is possible to estimate the game difficulty at the population level in multiple games. We applied the technique to two simple MTA games and showed that it could predict the relative difference in game difficulties experienced by players with different skills.