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arXiv:1802.10217cs.AI

Investigating Human Priors for Playing Video Games

Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L. Griffiths, Alexei A. Efros

Abstract

What makes humans so good at solving seemingly complex video games? Unlike computers, humans bring in a great deal of prior knowledge about the world, enabling efficient decision making. This paper investigates the role of human priors for solving video games. Given a sample game, we conduct a series of ablation studies to quantify the importance of various priors on human performance. We do this by modifying the video game environment to systematically mask different types of visual information that could be used by humans as priors. We find that removal of some prior knowledge causes a drastic degradation in the speed with which human players solve the game, e.g. from 2 minutes to over 20 minutes. Furthermore, our results indicate that general priors, such as the importance of objects and visual consistency, are critical for efficient game-play. Videos and the game manipulations are available at https://rach0012.github.io/humanRL_website/

arXiv comments
ICML 2018
Journal reference
ICML 2018
DOI
Subjects
cs.AIArtificial Intelligence cs.LGMachine Learning

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Discussed in comments3 threads

    • Indeed. Some work has actually been done to try to quantify how important various types of human prior knowledge are for learning video games: https://arxiv.org/pdf/1802.10217.pdf

      ki28iukgvdruView on HNParent context

    • This[1] paper does an excellent job gradually removing cultural priors from a simple videogame, and measuring the extent to which human and computer performance degrade. Their conclusion is that human ability to learn games fast depends on our ability to map them to real world…

      QuadmasterXLIIView on HNParent context

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