Abstract

Without balance in gamespaces, players will not like the gamespace and even worse, they will will not like the game. To prevent players from not liking the game, level designer create many iterations of each gamespace. For each iteration of a gamespace, playtest data is required. Rather than using human playtesting to generate the data required, we propose using agent-based modeling (ABM). ABM used in this context swaps out the humans during the playtesting and replaces them with artificial agents. To bring the artificial agents as close as possible to human-like behavior, we use machine learning agents. By training the machine learning agents, human-like behavior was achieved in a small set of scenarios. The data generated by the trained agents in gamespace can be used by designers to more efficiently create new iterations of their gamespaces.

Results/Implementation/Project Description

Conclusion

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