Automated MMORPG Testing – An Agent-Based Approach

Schatten, Markus; Đurić, Bogdan Okreaša; Tomičić, Igor; Ivković, Nikola

doi:10.1007/978-3-319-59930-4_38

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Following a mixed-initiative [27] approach, Butler et al [28] allowed the designer to specify the difficulty progression via a user interface while a constraint solver ensured the solvability of generated puzzles. Schatten et al [14] simulated large-scale dynamic agent systems to test quest solvability in MMORPGs. Pfau et al [13] introduced a generic adventure solver traversing point-and-click adventure games via reinforcement learning and reporting crashes, dead-ends and performance issues.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, AI can predict users' high-level behavior [8] (if they will leave the game, if they will make purchases) or their motivation levels [9] based on past trends in the userbase. Specifically with regards to automated playtesting, AI agents have been used to discover bugs or game crashes [10], constraint violations [11], [12] to identify dead-end game states [13] or unreachable states [14]. Notably, most of these agents follow ad-hoc heuristics and constraints and do not necessarily match how players act in their games.…”

Section: Introductionmentioning

confidence: 99%

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Dungeons & Replicants II: Automated Game Balancing Across Multiple Difficulty Dimensions via Deep Player Behavior Modeling

et al. 2023

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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.

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