RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games
This work addresses the labor-intensive challenge of balancing game content for commercial-level games, though it appears incremental by focusing on a specific scenario.
The authors tackled the problem of automated content balancing in boss raid games by proposing RaidEnv, a new game simulator with diverse content and benchmarks, which expands the frontiers of automatic game balancing and offers a practical framework for game production.
The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Although game designers intend to adjust the difficulty of game content, this is a repetitive, labor-intensive, and challenging process, especially for commercial-level games with extensive content. To address this issue, the game research community has explored automated game balancing using artificial intelligence (AI) techniques. However, previous studies have focused on limited game content and did not consider the importance of the generalization ability of playtesting agents when encountering content changes. In this study, we propose RaidEnv, a new game simulator that includes diverse and customizable content for the boss raid scenario in MMORPG games. Additionally, we design two benchmarks for the boss raid scenario that can aid in the practical application of game AI. These benchmarks address two open problems in automatic content balancing, and we introduce two evaluation metrics to provide guidance for AI in automatic content balancing. This novel game research platform expands the frontiers of automatic game balancing problems and offers a framework within a realistic game production pipeline.