The “Software Engineering for Games in Serious Contexts: Theories, Methods, Tools, and Experiences” book is now out! If you have the opportunity to read it there is a chapter co-authored by SEER Lab’s Michael Miljanovic and Jeremy Bradbury on “Engineering Adaptive Serious Games Using Machine Learning.”
In this chapter they discuss the development of new Machine Learning (ML)-based serious games (SGs) and present a generalized model for evolving existing SGs to use ML without needing to rebuild the game from scratch. In addition, to describing how to engineer ML-based SGs, they also highlight five common challenges encountered during our own development experiences, along with advice on how to address these challenges. Challenges discussed include:
- selection data for use in an ML model for SGs
- choosing game elements to adapt
- solving the cold start problem
- determining the frequency of adaptation
- testing that an adaptive game benefits from learning