In recent years, multiplayer games have gained significant popularity, but they also face many challenges. In particular, making games enjoyable for single players is essential for attracting a broad user base. To enable single-player enjoyment of multiplayer games, rule-based non-player characters (NPCs) have long been used to form teams with players. However, these NPCs often do not behave like humans and frequently act contrary to the human player's intentions. In this study, we propose a game AI that adopts human-like playstyles using behavioral data from human players. Subsequently, we conducted quantitative evaluations of human-like playstyles and qualitative assessments through cooperative play between the trained AI and human players. The results showed that the proposed method is a model capable of realizing human-like playstyles in both quantitative and qualitative evaluations.
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- Quote paper
- Masatoshi Fujiyama (Author), 2024, Proposal of Game-AI that Takes Human-Like Play Styles in Multiplayer RPGs, Munich, GRIN Verlag, https://www.grin.com/document/1690581
