Pairing Character Classes in a Deathmatch Shooter Game via a Deep-Learning Surrogate Model
This addresses game design automation for developers, but it is incremental as it applies existing deep-learning methods to a specific domain.
The paper tackles the problem of designing character classes for balanced, short-duration matches in shooter games by using a deep-learning surrogate model that maps game levels and class parameters to gameplay outcomes, and finds the system can generate classes for both computer-generated and human-authored levels.
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.