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

Autor:	Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzepecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Computer Science - Robotics Computer Science - Human-Computer Interaction Robotics (cs.RO) Human-Computer Interaction (cs.HC) Machine Learning (cs.LG)
Popis:	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. 18 pages; accepted at CHI 2023
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37e2eda5f059abc6bb31b254eb2f07db http://arxiv.org/abs/2303.02160 Zobrazit plný text záznamu