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DeepColor: Reinforcement Learning optimizes information efficiency and well-formedness in color name partitioning

Conference paper
Authors M. Kågebäck
D. Dubhashi
Asad Sayeed
Published in CogSci 2018, 40th annual Cognitive Science Society meeting, Madison Wisconsin USA, July 25-28 2018
ISBN 978-0-9911967-8-4
Publisher Cognitive Science Society
Place of publication Oakbrook Terrace, IL, USA
Publication year 2018
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Keywords color naming; world color survey; reinforcement learning
Subject categories Cognitive science, Computational linguistics, Linguistics


As observed in the World Color Survey (WCS), some universal properties can be identified in color naming schemes over a large number of languages. For example, Regier, Kay, and Khetrapal (2007) and Regier, Kemp, and Kay (2015); Gibson et al. (2017) recently explained these universal patterns in terms of near optimal color partitions and information theoretic measures of efficiency of communication. Here, we introduce a computational learning framework with multi-agent systems trained by reinforcement learning to investigate these universal properties. We compare the results with Regier et al. (2007, 2015) and show that our model achieves excellent quantitative agreement. This work introduces a multi-agent reinforcement learning framework as a powerful and versatile tool to investigate such semantic universals in many domains and contribute significantly to central questions in cognitive science.

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