Learning the patterns of balance in a multi-player shooter game

Abstract

A particular challenge of the game design process is when the designer is requested to orchestrate dissimilar elements of games such

as visuals, audio, narrative and rules to achieve a specic play experience. Within the domain of adversarial rst person shooter games,

for instance, a designer must be able to comprehend the dierences between the weapons available in the game, and appropriately cra a game level to take advantage of strengths and weaknesses of those

weapons. As an initial study towards computationally orchestrating dissimilar content generators in games, this paper presents a

computational model which can classify a matchup of a team-based shooter game as balanced or as favoring one or the other team. e computational model uses convolutional neural networks to learn how game balance is aected by the level, represented as an image, and each team’s weapon parameters. e model was trained on a corpus of over 50,000 simulated games with articial agents on a diverse set of levels created by 39 dierent generators. e results show that the fusion of levels, when processed by a convolutional neural network, and weapon parameters yields an accuracy far above the baseline but also improves accuracy compared to articial neural networks or models which use partial information, such as only the weapon or only the level as input.