Pairing Character Classes in a Deathmatch Shooter Game via a Deep-Learning Surrogate Model

Key insights

6 takeaways

01
Deep-learning surrogate models can replace computationally expensive simulations to predict deathmatch shooter outcomes, significantly accelerating the design iteration process.See it on page 1
02
Convolutional neural network (CNN) architectures outperform multi-layer perceptron models in predicting player scores and identifying design trends like the relationship between hit points and match duration.See it on page 4
03
Surrogate models function as automated design assistants that optimize character class parameters for both human-authored and procedurally generated map environments.See it on page 1
04
The current implementation faces a trade-off where high-speed surrogate evaluation results in lower precision for duration predictions, particularly in complex or dense map layouts.See it on page 9
05
Integrating surrogate models with evolutionary algorithms enables scalable, multi-objective optimization for balancing competitive shooter mechanics.See it on page 10
06
This data-driven methodology allows developers to explore vast parameter spaces that are otherwise prohibitive to test through traditional gameplay simulation.See it on page 9

Summary

Deep-learning surrogate models provide a robust framework for balancing character classes in deathmatch shooter games by predicting match outcomes without the need for exhaustive, computationally expensive simulations. By analyzing the intricate relationship between character class parameters and level architecture, these models function as automated design assistants capable of optimizing content for both human-authored and procedurally generated environments. This approach significantly accelerates the design iteration process, allowing developers to explore vast parameter spaces that would otherwise be prohibitive to test through traditional gameplay simulation.

The implementation relies on a convolutional neural network architecture that demonstrates high proficiency in predicting player scores and identifying intuitive design trends, such as the correlation between increased hit points and extended match duration. While the model exhibits superior performance compared to multi-layer perceptron alternatives, it faces challenges regarding the precision of duration predictions, particularly within dense or complex map layouts where variance is higher. These limitations highlight a current trade-off between the speed of the surrogate evaluation and the absolute accuracy of temporal predictions in highly variable game states.

Ultimately, the integration of these surrogate models with evolutionary algorithms offers a scalable solution for game balancing. Future advancements in this field should prioritize multi-objective optimization to better reconcile the discrepancies between score and duration metrics. By serving as a co-creative tool, this technology empowers designers to refine game content more efficiently, ensuring that character matchups remain balanced across diverse and evolving play spaces. This methodology represents a significant shift toward data-driven design, providing a viable pathway for automating the complex task of tuning competitive shooter mechanics.