Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games - Milani, Stephanie; Juliani, Arthur; Momennejad, Ida; Stevenson, Raluca; Rzepecki, Jaroslaw; Shaw, Alison; Costello, Gavin; Fang, Fei; Devlin, Sam; Hofmann, Katja (CHI, 2023)
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.