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

• URL: http://arxiv.org/abs/2303.02160v1
• Date: Thu, 2 Mar 2023 18:59:04 GMT
• Title: Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
• Authors: Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzpecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann
• Abstract summary: We collect hundreds of crowd-sourced assessments comparing the human-likeness of navigation behavior generated by our agent and baseline AI agents. Our proposed agent passes a Turing Test, while the baseline agents do not. This work provides insights into the characteristics that people consider human-like in the context of goal-directed video game navigation.
• Score: 36.96985093527702
• License: http://creativecommons.org/licenses/by/4.0/
• 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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