类人导航行为：一个统计评估框架
Human-Like Navigation Behavior: A Statistical Evaluation Framework
摘要：
深度强化学习的最新进展带来了令人印象深刻的展示，展示了能够进行复杂智能行为的高度熟练的人工智能体。在视频游戏中，这些人工智能体越来越多地被部署为不可玩角色（NPC），旨在增强人类玩家的体验。然而，虽然已经表明NPC的令人信服的人类行为会增加对视频游戏的参与，但人工智能行为的可信度通常仅通过其对给定任务的熟练程度来衡量。最近的研究表明，仅凭熟练程度并不足以辨别人类的行为。出于这一动机，我们建立了一个非参数双样本假设检验，旨在比较人工代理的行为，人类球员。我们表明，由此产生的$p$值不仅符合匿名的人类判断的类人行为，但它也可以被用来作为一个衡量相似性。
英文摘要：
Recent advancements in deep reinforcement learning have brought forth an impressive display of highly skilled artificial agents capable of complex intelligent behavior. In video games, these artificial agents are increasingly deployed as non-playable characters (NPCs) designed to enhance the experience of human players. However, while it has been shown that the convincing human-like behavior of NPCs leads to increased engagement in video games, the believability of an artificial agent's behavior is most often measured solely by its proficiency at a given task. Recent work has hinted that proficiency alone is not sufficient to discern human-like behavior. Motivated by this, we build a non-parametric two-sample hypothesis test designed to compare the behaviors of artificial agents to those of human players. We show that the resulting $p$-value not only aligns with anonymous human judgment of human-like behavior, but also that it can be used as a measure of similarity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
