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Recursive Metropolis-Hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models
In the studies on symbol emergence and emergent communication in a population of agents, a computational model was employed in which agents participate in various language games. Among these, the Metropolis-Hastings naming game (MHNG) possesses a notable mathematical property: symbol emergence throu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619661/ https://www.ncbi.nlm.nih.gov/pubmed/37920571 http://dx.doi.org/10.3389/frai.2023.1229127 |
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author | Inukai, Jun Taniguchi, Tadahiro Taniguchi, Akira Hagiwara, Yoshinobu |
author_facet | Inukai, Jun Taniguchi, Tadahiro Taniguchi, Akira Hagiwara, Yoshinobu |
author_sort | Inukai, Jun |
collection | PubMed |
description | In the studies on symbol emergence and emergent communication in a population of agents, a computational model was employed in which agents participate in various language games. Among these, the Metropolis-Hastings naming game (MHNG) possesses a notable mathematical property: symbol emergence through MHNG is proven to be a decentralized Bayesian inference of representations shared by the agents. However, the previously proposed MHNG is limited to a two-agent scenario. This paper extends MHNG to an N-agent scenario. The main contributions of this paper are twofold: (1) we propose the recursive Metropolis-Hastings naming game (RMHNG) as an N-agent version of MHNG and demonstrate that RMHNG is an approximate Bayesian inference method for the posterior distribution over a latent variable shared by agents, similar to MHNG; and (2) we empirically evaluate the performance of RMHNG on synthetic and real image data, i.e., YCB object dataset, enabling multiple agents to develop and share a symbol system. Furthermore, we introduce two types of approximations—one-sample and limited-length—to reduce computational complexity while maintaining the ability to explain communication in a population of agents. The experimental findings showcased the efficacy of RMHNG as a decentralized Bayesian inference for approximating the posterior distribution concerning latent variables, which are jointly shared among agents, akin to MHNG, although the improvement in ARI and κ coefficient is smaller in the real image dataset condition. Moreover, the utilization of RMHNG elucidated the agents' capacity to exchange symbols. Furthermore, the study discovered that even the computationally simplified version of RMHNG could enable symbols to emerge among the agents. |
format | Online Article Text |
id | pubmed-10619661 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106196612023-11-02 Recursive Metropolis-Hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models Inukai, Jun Taniguchi, Tadahiro Taniguchi, Akira Hagiwara, Yoshinobu Front Artif Intell Artificial Intelligence In the studies on symbol emergence and emergent communication in a population of agents, a computational model was employed in which agents participate in various language games. Among these, the Metropolis-Hastings naming game (MHNG) possesses a notable mathematical property: symbol emergence through MHNG is proven to be a decentralized Bayesian inference of representations shared by the agents. However, the previously proposed MHNG is limited to a two-agent scenario. This paper extends MHNG to an N-agent scenario. The main contributions of this paper are twofold: (1) we propose the recursive Metropolis-Hastings naming game (RMHNG) as an N-agent version of MHNG and demonstrate that RMHNG is an approximate Bayesian inference method for the posterior distribution over a latent variable shared by agents, similar to MHNG; and (2) we empirically evaluate the performance of RMHNG on synthetic and real image data, i.e., YCB object dataset, enabling multiple agents to develop and share a symbol system. Furthermore, we introduce two types of approximations—one-sample and limited-length—to reduce computational complexity while maintaining the ability to explain communication in a population of agents. The experimental findings showcased the efficacy of RMHNG as a decentralized Bayesian inference for approximating the posterior distribution concerning latent variables, which are jointly shared among agents, akin to MHNG, although the improvement in ARI and κ coefficient is smaller in the real image dataset condition. Moreover, the utilization of RMHNG elucidated the agents' capacity to exchange symbols. Furthermore, the study discovered that even the computationally simplified version of RMHNG could enable symbols to emerge among the agents. Frontiers Media S.A. 2023-10-18 /pmc/articles/PMC10619661/ /pubmed/37920571 http://dx.doi.org/10.3389/frai.2023.1229127 Text en Copyright © 2023 Inukai, Taniguchi, Taniguchi and Hagiwara. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence Inukai, Jun Taniguchi, Tadahiro Taniguchi, Akira Hagiwara, Yoshinobu Recursive Metropolis-Hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models |
title | Recursive Metropolis-Hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models |
title_full | Recursive Metropolis-Hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models |
title_fullStr | Recursive Metropolis-Hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models |
title_full_unstemmed | Recursive Metropolis-Hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models |
title_short | Recursive Metropolis-Hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models |
title_sort | recursive metropolis-hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619661/ https://www.ncbi.nlm.nih.gov/pubmed/37920571 http://dx.doi.org/10.3389/frai.2023.1229127 |
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