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The balance of game content significantly impacts the gaming experience.Unbalanced game content diminishes engagement or increases frustration becauseof repetitive failure. Although game designers intend to adjust the difficultyof game content, this is a repetitive, labor-intensive, and challengingprocess, especially for commercial-level games with extensive content. Toaddress this issue, the game research community has explored automated gamebalancing using artificial intelligence (AI) techniques. However, previousstudies have focused on limited game content and did not consider theimportance of the generalization ability of playtesting agents whenencountering content changes. In this study, we propose RaidEnv, a new gamesimulator that includes diverse and customizable content for the boss raidscenario in MMORPG games. Additionally, we design two benchmarks for the bossraid scenario that can aid in the practical application of game AI. Thesebenchmarks address two open problems in automatic content balancing, and weintroduce two evaluation metrics to provide guidance for AI in automaticcontent balancing. This novel game research platform expands the frontiers ofautomatic game balancing problems and offers a framework within a realisticgame production pipeline.

RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games