mlsanityData to 14 Oct 2026

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

arXiv 2303.02160 ↗First posted 2 Mar 2023cs.HC cs.LG cs.ROcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 23 of its 98 references that are in the map. First posted on arXiv 2 Mar 2023.

Builds on

23 of 98 references.

References · 23

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11 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 5; 2026: 1.

11 papers.