Dungeons & replicants II : automated game balancing across multiple difficulty dimensions via deep player behavior modeling

Abstract

Video game testing has become a major investment of time, labor and expense in the game industry. Particularly the balancing of in-game units, characters and classes can cause long-lasting issues that persist years after a game’s launch. While approaches incorporating artificial intelligence have already shown successes in reducing manual effort and enhancing game development processes, most of these draw on heuristic, generalized or optimal behavior routines, while actual low-level decisions from individual players and their resulting playing styles are rarely considered. In this paper, we apply Deep Player Behavior Modeling to turn atomic actions of 213 players from 6 months of single-player instances within the MMORPG Aion into generative models that capture and reproduce particular playing strategies. In a subsequent simulation, the resulting generative agents (“replicants”) were tested against common NPC opponent types of MMORPGs that iteratively increased in difficulty, respective to the primary factor that constitutes this enemy type (Melee, Ranged, Rogue, Buffer, Debuffer, Healer, Tank or Group). As a result, imbalances between classes as well as strengths and weaknesses regarding particular combat challenges could be identified and regulated automatically.