Considerations for Comparing Video Game AI Agents with Humans

Christopher R. Madan
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Christopher R. Madan: School of Psychology, University of Nottingham, Nottingham NG7 2RD, UK

Challenges, 2020, vol. 11, issue 2, 1-12

Abstract: 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.

Keywords: AlphaStar; explainable AI; StarCraft; Rubik’s Cube; AlphaGo; machine learning; reinforcement learning; robot psychology (search for similar items in EconPapers)
JEL-codes: A00 C00 Z00 (search for similar items in EconPapers)
Date: 2020
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