Abstract
Procedural content generation is a technique used in many games. But if the game generates too much content this way, it can be difficult to test all possible scenarios with players before launching the game. In these cases, an AI agent is used to test the whole content, but it only performs basic validations and does not gives us the subjective feedback of a player. In this situation, it seems there is no viable way of testing large amounts of generated content. We created a methodology that uses an affective agent, to test a game using the personality and skill of a player to give us some emotional feedback on the playtesting session. The affective agent is a combination of an affective agent architecture and a personality model. We present PLEASED, an implementation of our methodology that was used as an example of our approach. To evaluate how effective is our approach, we designed and developed a game to be evaluated by PLEASED. The effectiveness of our system was measured by comparing feedback from PLEASED with players' feedback. Our results suggest that PLEASED is more suitable to simulate “casual" players than “hardcore" players and that “hardcore" players follow different criteria to evaluate a game when compared to casual ones.