{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T15:24:22Z","timestamp":1781969062405,"version":"3.54.5"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T00:00:00Z","timestamp":1697587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>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 <jats:italic>N<\/jats:italic>-agent scenario. The main contributions of this paper are twofold: (1) we propose the recursive Metropolis-Hastings naming game (RMHNG) as an <jats:italic>N<\/jats:italic>-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\u2014one-sample and limited-length\u2014to 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 <jats:italic>\u03ba<\/jats:italic> 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.<\/jats:p>","DOI":"10.3389\/frai.2023.1229127","type":"journal-article","created":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T08:57:06Z","timestamp":1697619426000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Recursive Metropolis-Hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models"],"prefix":"10.3389","volume":"6","author":[{"given":"Jun","family":"Inukai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tadahiro","family":"Taniguchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akira","family":"Taniguchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshinobu","family":"Hagiwara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,10,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1126\/science.286.5439.509","article-title":"Emergence of scaling in random networks","volume":"286","author":"Barab\u00e1si","year":"1999","journal-title":"Science"},{"key":"B2","doi-asserted-by":"crossref","first-page":"981","DOI":"10.18653\/v1\/D18-1119","article-title":"\u201cHow agents see things: On visual representations in an emergent language game,\u201d","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Bouchacourt","year":"2018"},{"key":"B3","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1109\/ICAR.2015.7251504","article-title":"\u201cThe YCB object and model set: Towards common benchmarks for manipulation research,\u201d","volume-title":"2015 International Conference on Advanced Robotics","author":"Calli","year":"2015"},{"key":"B4","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/9320.001.0001","volume-title":"Developmental Robotics: From Babies to Robots","author":"Cangelosi","year":"2015"},{"key":"B5","unstructured":"\u201cEmergent communication at scale,\u201d\n            ChaabouniR.\n            StrubF.\n            Altch\u00e9F.\n            TarassovE.\n            TallecC.\n            DavoodiE.\n          International Conference on Learning Representations2022"},{"key":"B6","volume-title":"Semiotics the Basics","author":"Chandler","year":"2002"},{"key":"B7","first-page":"15750","article-title":"\u201cExploring simple Siamese representation learning,\u201d","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Chen","year":"2021"},{"key":"B8","unstructured":"\u201cCompositional obverter communication learning from raw visual input,\u201d\n            ChoiE.\n            LazaridouA.\n            de FreitasN.\n          The International Conference on Learning Representation2018"},{"key":"B9","volume-title":"The Language Game: How Improvisation Created Language and Changed the World","author":"Christiansen","year":"2022"},{"key":"B10","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. 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