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arXiv · 2303.02160

Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games

Stephanie Milani·Arthur Juliani·Ida Momennejad·Raluca Georgescu·Jaroslaw Rzpecki·Alison Shaw·Gavin Costello·Fei Fang·Sam Devlin·Katja Hofmann
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Abstract

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.

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Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzpecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann. 2023-03-02. Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games. https://doi.org/10.1145/3544548.3581348

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