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We propose a fully decentralized multi-agent world model that enables both symbol emergence for communication and coordinated behavior through temporal extension of collective predictive coding.
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2018
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2018
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2019
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2023
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H. Ebara, T. Nakamura, A. Taniguchi, and T. Taniguchi, “Multi-agent reinforcement learning with emergent communication using discrete and indifferentiable message,” in 2023 15th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter) , 2023, pp. 366–371
2023
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2024
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2019
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2021
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T. Lin, J. Huh, C. Stauffer, S. N. Lim, and P. Isola, “Learning to ground multi-agent communication with autoencoders,” in Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, Eds., vol. 34. Curran Associates, Inc., 2021, pp. 15 230–15 242. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2021/file/80fee67c8a4c4989bf8a580b4bbb0cd2-Paper.pdf
2021
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Y. Wang, F. Zhong, J. Xu, and Y. Wang, “Tom2c: Target-oriented multi-agent communication and cooperation with theory of mind,” in International Conference on Learning Representations , 2022
2022
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M. Wen, J. G. Kuba, R. Lin, W. Zhang, Y. Wen, J. Wang, and Y. Yang, “Multi-agent reinforcement learning is a sequence modeling problem,” in Proceedings of the 36th International Conference on Neural Information Processing Systems , ser. NIPS ’22. Red Hook, NY, USA: Curran Associates Inc., 2022
2022
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N. Brandizzi, “Toward more human-like ai communication: A review of emergent communication research,” IEEE Access , vol. 11, pp. 142 317–142 340, 2023
2023
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T. Taniguchi, S. Murata, M. Suzuki, D. Ognibene, P. Lanillos, E. Ugur, L. Jamone, T. Nakamura, A. Ciria, B. Lara, and G. P. and, “World models and predictive coding for cognitive and developmental robotics: frontiers and challenges,” Advanced Robotics , vol. 37, no. 13, pp. 780–806, 2023. [Online]. Available: https://doi.org/10.1080/01691864.2023.2225232
2023
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R. Okumura, T. Taniguchi, Y. Hagiwara, and A. Taniguchi, “Metropolis-hastings algorithm in joint-attention naming game: experimental semiotics study,” Frontiers in Artificial Intelligence , vol. Volume 6 - 2023, 2023. [Online]. Available: https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1235231
2023
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T. Taniguchi, Y. Yoshida, Y. Matsui, N. L. Hoang, A. Taniguchi, and Y. H. and, “Emergent communication through metropolis-hastings naming game with deep generative models,” Advanced Robotics , vol. 37, no. 19, pp. 1266–1282, 2023. [Online]. Available: https://doi.org/10.1080/01691864.2023.2260856
2023
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2024
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T. Taniguchi, “Collective predictive coding hypothesis: symbol emergence as decentralized bayesian inference,” Frontiers in Robotics and AI , vol. Volume 11 - 2024, 2024. [Online]. Available: https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2024.1353870
2024
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2024
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2024
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2024
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2025
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H. Wang, B. Chen, T. Zhang, and B. Wang, “Learning to communicate through implicit communication channels,” in The Thirteenth International Conference on Learning Representations , 2025. [Online]. Available: https://openreview.net/forum?id=wm5wwAdiEt
2025
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T. Taniguchi, S. Takagi, J. Otsuka, Y. Hayashi, and H. T. Hamada, “Collective predictive coding as model of science: formalizing scientific activities towards generative science,” Royal Society Open Science , vol. 12, no. 6, p. 241678, 2025. [Online]. Available: https://royalsocietypublishing.org/doi/abs/10.1098/rsos.241678
2025
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