Enterprise AI Analysis
Revolutionizing Pen & Paper RPG Design with AI-Driven Monster Level Estimation
This master's thesis explores the application of machine learning techniques to automate monster level estimation in Pathfinder Second Edition pen & paper RPGs. The work develops an accurate and efficient system to aid game designers and Game Masters. A dedicated dataset was created, and various ML models, including classical regression and specialized ordinal regression, were evaluated using chronological data splits and expanding window validation to ensure real-world applicability. Experiments demonstrate that ML models can reliably predict monster levels, offering significant potential for practical applications in RPG content development, such as balancing encounters and generating new monsters, ultimately enhancing player experience and accelerating the design process.
Key Executive Impact
Leverage AI to streamline game development and enhance player experience with precision-balanced encounters.
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Error Reduction (MAE^M)
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Prediction Accuracy (@1 Level)
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Development Cycle Speed-up
Deep Analysis & Enterprise Applications
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AI for Enhanced Game Development
This research demonstrates the significant potential of machine learning to transform the design and balancing of Pen & Paper RPGs. By automating the complex task of monster level estimation, game developers can achieve greater efficiency, consistency, and player satisfaction. The findings highlight that sophisticated ML models can outperform traditional manual methods, providing accurate predictions that contribute to a more dynamic and balanced gaming ecosystem.
75.7%
Reduction in Macroaveraged MAE error compared to baseline human-inspired model.
Enterprise Process Flow: ML for RPG Monster Design
Data Gathering
→
Feature Engineering
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Model Training
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Evaluation
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Prediction
| Aspect | Classical Regression with Rounding | Dedicated Ordinal Regression Models |
|---|---|---|
| Core Idea | Predict continuous values, then map to discrete categories. | Directly model the ordered nature of the target variable. |
| Model Flexibility | Leverages a vast array of standard regression models (Linear, RF, LightGBM, SVM). | Requires specialized algorithms or adaptations of standard classifiers. |
| Ordinality Handling | Ordinality is handled post-prediction via rounding, which may not always respect the underlying order perfectly. | Ordinal structure is intrinsically incorporated into the model's design and loss function. |
| Performance on Imbalanced Data | Can be sensitive to class imbalance, often requiring macro-averaging for fair evaluation. | Often designed with mechanisms (e.g., weighted loss, cumulative probabilities) to better handle class imbalance. |
| Complexity | Simpler to implement using off-the-shelf regression tools, but threshold tuning can add complexity. | Can be more complex to implement and understand, but offers more precise control over ordinal properties. |
| Typical Use Cases | Quick prototyping, when performance gains from dedicated ordinal models are minimal. | When explicit preservation of ordinal relationships and robust handling of imbalanced categories are critical. |
Impact on RPG Game Design Workflow
This work demonstrates how ML can significantly reduce the development cycle for new monsters by providing an initial estimator for their challenge level. Instead of extensive manual playtesting (2-6 hours per session), designers can use these models to quickly balance encounters. Game Masters can also benefit by creating custom, balanced adversaries without the need for comprehensive playtesting. This integrates AI-assisted design tools into the TTRPG ecosystem, enhancing player experience and content creation efficiency.
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03. Integration & Deployment
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04. Monitoring & Optimization
Continuous monitoring of AI performance, iterative improvements, and scaling for long-term value.
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