Language games meet multi-agent reinforcement learning: A case study for the naming game

Research output: Contribution to journalArticlepeer-review

Abstract

Today, computational models of emergent communication in populations of autonomous agents are studied through two main methodological paradigms: multi-agent reinforcement learning (MARL) and the language game paradigm. While both paradigms share their main objectives and employ strikingly similar methods, the interaction between both communities has so far been surprisingly limited. This can to a large extent be ascribed to the use of different terminologies and experimental designs, which sometimes hinder the detection and interpretation of one another’s results and progress. Through this paper, we aim to remedy this situation by (1) formulating the challenge of re-conceptualising the language game experimental paradigm in the framework of MARL, and by (2) providing both an alignment between their terminologies and a MARL-based reformulation of the canonical naming game experiment. Tackling this challenge will enable future language game experiments to benefit from the rapid and promising methodological advances in the MARL community, while it will enable future MARL experiments on learning emergent communication to benefit from the insights and results gained through language game experiments. We strongly believe that this cross-pollination has the potential to lead to major breakthroughs in the modelling of how human-like languages can emerge and evolve in multi-agent systems.
Original languageEnglish
Pages (from-to)213-223
Number of pages11
JournalJournal of Language Evolution
Volume7
Issue number2
DOIs
Publication statusPublished - Jul 2022

Bibliographical note

Funding Information:
The research reported on in this paper was financed by the Research Foundation Flanders (FWO - Vlaanderen) through postdoctoral grants awarded to Paul Van Eecke (75929) and Roxana Rădulescu (1286223N), and by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 951846 (MUHAI - https://www.muhai.org ).

Publisher Copyright:
© 2023 The Author(s). Published by Oxford University Press.

Copyright:
Copyright 2023 Elsevier B.V., All rights reserved.

Keywords

  • language games
  • multi-agent reinforcement learning
  • naming game
  • evolutionary linguistics
  • emergent communication

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