Closing the Affective Loop via Experience-Driven Reinforcement Learning Designers
This work addresses the challenge of affect-aware human-computer interaction by enabling personalized content generation in domains like gaming, though it is incremental as it builds on existing experience-driven procedural content generation frameworks.
The authors tackled the problem of autonomously generating content tailored to specific affective patterns, such as arousal traces in racing games, by proposing a novel reinforcement learning framework called EDRL. The result showed that EDRL accurately generates affect-driven racing game levels according to a designer's style and outperforms search-based methods for personalized content generation.
Autonomously tailoring content to a set of predetermined affective patterns has long been considered the holy grail of affect-aware human-computer interaction at large. The experience-driven procedural content generation framework realises this vision by searching for content that elicits a certain experience pattern to a user. In this paper, we propose a novel reinforcement learning (RL) framework for generating affect-tailored content, and we test it in the domain of racing games. Specifically, the experience-driven RL (EDRL) framework is given a target arousal trace, and it then generates a racetrack that elicits the desired affective responses for a particular type of player. EDRL leverages a reward function that assesses the affective pattern of any generated racetrack from a corpus of arousal traces. Our findings suggest that EDRL can accurately generate affect-driven racing game levels according to a designer's style and outperforms search-based methods for personalised content generation. The method is not only directly applicable to game content generation tasks but also employable broadly to any domain that uses content for affective adaptation.