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

TL;DR

This paper presents a deep learning surrogate model that predicts gameplay outcomes based on level structure and character classes, enabling automated content generation for balanced matches in shooter games.

Contribution

It introduces a novel deep learning model that maps game level and character class parameters to gameplay outcomes, facilitating automated content design in shooter games.

Findings

Model effectively predicts gameplay outcomes from level and class data.

System can generate character classes for both AI-generated and human-designed levels.

Demonstrates potential for automated balancing and content creation in shooter games.

Abstract

This paper introduces a surrogate model of gameplay that learns the mapping between different game facets, and applies it to a generative system which designs new content in one of these facets. Focusing on the shooter game genre, the paper explores how deep learning can help build a model which combines the game level structure and the game's character class parameters as input and the gameplay outcomes as output. The model is trained on a large corpus of game data from simulations with artificial agents in random sets of levels and class parameters. The model is then used to generate classes for specific levels and for a desired game outcome, such as balanced matches of short duration. Findings in this paper show that the system can be expressive and can generate classes for both computer generated and human authored levels.