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- W4296426236 abstract "Recent advancements in deep reinforcement learning have demonstrated highly skilled agents that are capable of complex behavior. In video games, such agents are increasingly deployed as non-playable characters (NPCs) to enhance the gaming experience, as convincing human-like behavior is known to increase player engagement. However, the believability of an agent’s behavior is often measured solely by its proficiency at a given task, which alone is not sufficient to discern human-likeness. In this paper, we build a non-parametric two-sample hypothesis test to compare the behaviors of NPCs to those of human players using distributions of their movement patterns. We show that the resulting p-value metric not only aligns with anonymous human judgment of human-like behavior, but it can also be used as a measure of similarity." @default.
- W4296426236 created "2022-09-20" @default.
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- W4296426236 date "2022-08-21" @default.
- W4296426236 modified "2023-10-16" @default.
- W4296426236 title "Evaluating Navigation Behavior of Agents in Games using Non-Parametric Statistics" @default.
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- W4296426236 doi "https://doi.org/10.1109/cog51982.2022.9893600" @default.
- W4296426236 hasPublicationYear "2022" @default.
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