Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
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DOI：
10.1145/3544548.3581348
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Stephanie Milani;Arthur Juliani;I. Momennejad;Raluca Georgescu;Jaroslaw Rzepecki;Alison Shaw;Gavin Costello;Fei Fang;Sam Devlin;Katja Hofmann
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文献类型：
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作者：
Stephanie Milani;Arthur Juliani;I. Momennejad;Raluca Georgescu;Jaroslaw Rzepecki;Alison Shaw;Gavin Costello;Fei Fang;Sam Devlin;Katja Hofmann
我们的目标是了解人们如何在视频游戏中评估由人和人工智能（AI）代理产生的导航中的人类相似性。为此，我们提出了一种新的AI代理，其目标是生成更多类似人类的行为。我们收集了数百个众包评估，将我们的代理和基线AI代理生成的导航行为与人类生成的行为进行比较。我们提出的代理通过图灵测试，而基线代理没有。通过图灵测试，我们的意思是人类法官无法定量区分一个人的视频和一个AI代理导航。为了了解人们认为什么构成了类似人类的导航，我们广泛分析了这些评估的理由。这项工作提供了人们在目标导向的视频游戏导航背景下认为与人类相似的特征的见解，这是进一步改善人类与AI代理交互的关键一步。
We aim to understand how people assess human likeness in navigation produced by people and artificially intelligent (AI) agents in a video game. To this end, we propose a novel AI agent with the goal of generating more human-like behavior. We collect hundreds of crowd-sourced assessments comparing the human-likeness of navigation behavior generated by our agent and baseline AI agents with human-generated behavior. Our proposed agent passes a Turing Test, while the baseline agents do not. By passing a Turing Test, we mean that human judges could not quantitatively distinguish between videos of a person and an AI agent navigating. To understand what people believe constitutes human-like navigation, we extensively analyze the justifications of these assessments. This work provides insights into the characteristics that people consider human-like in the context of goal-directed video game navigation, which is a key step for further improving human interactions with AI agents.