Application of predictive machine learning in pen & paper RPG game design
This work addresses a specific challenge for RPG publishers by providing automated tools to reduce time and resource costs in game design, though it is incremental as it builds on existing methods.
The paper tackled the problem of automating challenge level estimation for opponents in pen and paper RPGs, which currently relies on manual methods, by evaluating state-of-the-art ordinal regression techniques and developing a dedicated dataset and human-inspired benchmark model.
In recent years, the pen and paper RPG market has experienced significant growth. As a result, companies are increasingly exploring the integration of AI technologies to enhance player experience and gain a competitive edge. One of the key challenges faced by publishers is designing new opponents and estimating their challenge level. Currently, there are no automated methods for determining a monster's level; the only approaches used are based on manual testing and expert evaluation. Although these manual methods can provide reasonably accurate estimates, they are time-consuming and resource-intensive. Level prediction can be approached using ordinal regression techniques. This thesis presents an overview and evaluation of state-of-the-art methods for this task. It also details the construction of a dedicated dataset for level estimation. Furthermore, a human-inspired model was developed to serve as a benchmark, allowing comparison between machine learning algorithms and the approach typically employed by pen and paper RPG publishers. In addition, a specialized evaluation procedure, grounded in domain knowledge, was designed to assess model performance and facilitate meaningful comparisons.