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Considerations for Comparing Video Game AI Agents with Humans

School of Psychology, University of Nottingham, Nottingham NG7 2RD, UK
Challenges 2020, 11(2), 18; https://doi.org/10.3390/challe11020018
Received: 25 July 2020 / Revised: 17 August 2020 / Accepted: 17 August 2020 / Published: 20 August 2020
Video games are sometimes used as environments to evaluate AI agents’ ability to develop and execute complex action sequences to maximize a defined reward. However, humans cannot match the fine precision of the timed actions of AI agents; in games such as StarCraft, build orders take the place of chess opening gambits. However, unlike strategy games, such as chess and Go, video games also rely heavily on sensorimotor precision. If the “finding” was merely that AI agents have superhuman reaction times and precision, none would be surprised. The goal is rather to look at adaptive reasoning and strategies produced by AI agents that may replicate human approaches or even result in strategies not previously produced by humans. Here, I will provide: (1) an overview of observations where AI agents are perhaps not being fairly evaluated relative to humans, (2) a potential approach for making this comparison more appropriate, and (3) highlight some important recent advances in video game play provided by AI agents. View Full-Text
Keywords: AlphaStar; explainable AI; StarCraft; Rubik’s Cube; AlphaGo; machine learning; reinforcement learning; robot psychology AlphaStar; explainable AI; StarCraft; Rubik’s Cube; AlphaGo; machine learning; reinforcement learning; robot psychology
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Madan, C.R. Considerations for Comparing Video Game AI Agents with Humans. Challenges 2020, 11, 18.

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