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Hoang NL, Taniguchi T, Hagiwara Y, Taniguchi A. Emergent communication of multimodal deep generative models based on Metropolis-Hastings naming game. Front Robot AI 2024; 10:1290604. [PMID: 38356917 PMCID: PMC10864618 DOI: 10.3389/frobt.2023.1290604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/18/2023] [Indexed: 02/16/2024] Open
Abstract
Deep generative models (DGM) are increasingly employed in emergent communication systems. However, their application in multimodal data contexts is limited. This study proposes a novel model that combines multimodal DGM with the Metropolis-Hastings (MH) naming game, enabling two agents to focus jointly on a shared subject and develop common vocabularies. The model proves that it can handle multimodal data, even in cases of missing modalities. Integrating the MH naming game with multimodal variational autoencoders (VAE) allows agents to form perceptual categories and exchange signs within multimodal contexts. Moreover, fine-tuning the weight ratio to favor a modality that the model could learn and categorize more readily improved communication. Our evaluation of three multimodal approaches - mixture-of-experts (MoE), product-of-experts (PoE), and mixture-of-product-of-experts (MoPoE)-suggests an impact on the creation of latent spaces, the internal representations of agents. Our results from experiments with the MNIST + SVHN and Multimodal165 datasets indicate that combining the Gaussian mixture model (GMM), PoE multimodal VAE, and MH naming game substantially improved information sharing, knowledge formation, and data reconstruction.
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Affiliation(s)
- Nguyen Le Hoang
- Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
| | - Tadahiro Taniguchi
- College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
| | - Yoshinobu Hagiwara
- Research Organization of Science and Technology, Ritsumeikan University, Kusatsu, Shiga, Japan
| | - Akira Taniguchi
- College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
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Okumura R, Taniguchi T, Hagiwara Y, Taniguchi A. Metropolis-Hastings algorithm in joint-attention naming game: experimental semiotics study. Front Artif Intell 2023; 6:1235231. [PMID: 38116389 PMCID: PMC10728479 DOI: 10.3389/frai.2023.1235231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 11/13/2023] [Indexed: 12/21/2023] Open
Abstract
We explore the emergence of symbols during interactions between individuals through an experimental semiotic study. Previous studies have investigated how humans organize symbol systems through communication using artificially designed subjective experiments. In this study, we focused on a joint-attention-naming game (JA-NG) in which participants independently categorized objects and assigned names while assuming their joint attention. In the Metropolis-Hastings naming game (MHNG) theory, listeners accept provided names according to the acceptance probability computed using the Metropolis-Hastings (MH) algorithm. The MHNG theory suggests that symbols emerge as an approximate decentralized Bayesian inference of signs, which is represented as a shared prior variable if the conditions of the MHNG are satisfied. This study examines whether human participants exhibit behavior consistent with the MHNG theory when playing the JA-NG. By comparing human acceptance decisions of a partner's naming with acceptance probabilities computed in the MHNG, we tested whether human behavior is consistent with the MHNG theory. The main contributions of this study are twofold. First, we reject the null hypothesis that humans make acceptance judgments with a constant probability, regardless of the acceptance probability calculated by the MH algorithm. The results of this study show that the model with acceptance probability computed by the MH algorithm predicts human behavior significantly better than the model with a constant probability of acceptance. Second, the MH-based model predicted human acceptance/rejection behavior more accurately than four other models (i.e., Constant, Numerator, Subtraction, Binary). Among the models compared, the model using the MH algorithm, which is the only model with the mathematical support of decentralized Bayesian inference, predicted human behavior most accurately, suggesting that symbol emergence in the JA-NG can be explained by the MHNG.
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Affiliation(s)
- Ryota Okumura
- Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
| | - Tadahiro Taniguchi
- College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
| | - Yoshinobu Hagiwara
- Research Organization of Science and Technology, Ritsumeikan University, Kusatsu, Japan
| | - Akira Taniguchi
- College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
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Inukai J, Taniguchi T, Taniguchi A, Hagiwara Y. Recursive Metropolis-Hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models. Front Artif Intell 2023; 6:1229127. [PMID: 37920571 PMCID: PMC10619661 DOI: 10.3389/frai.2023.1229127] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/22/2023] [Indexed: 11/04/2023] Open
Abstract
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.
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Affiliation(s)
- Jun Inukai
- Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
| | - Tadahiro Taniguchi
- Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
| | - Akira Taniguchi
- Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
| | - Yoshinobu Hagiwara
- Research Organization of Science and Technology, Ritsumeikan University, Kusatsu, Japan
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Nishikawa J, Morita J. Cognitive model of phonological awareness focusing on errors and formation process through Shiritori. Adv Robot 2022. [DOI: 10.1080/01691864.2022.2029763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Jumpei Nishikawa
- Graduate School of Integrated Science and Technology, Shizuoka University, Shizuoka, Japan
| | - Junya Morita
- Graduate School of Integrated Science and Technology, Shizuoka University, Shizuoka, Japan
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Hagiwara Y, Furukawa K, Taniguchi A, Taniguchi T. Multiagent multimodal categorization for symbol emergence: emergent communication via interpersonal cross-modal inference. Adv Robot 2022. [DOI: 10.1080/01691864.2022.2029721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Yoshinobu Hagiwara
- College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
| | - Kazuma Furukawa
- College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
| | - Akira Taniguchi
- College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
| | - Tadahiro Taniguchi
- College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
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Kuniyasu R, Nakamura T, Taniguchi T, Nagai T. Robot Concept Acquisition Based on Interaction Between Probabilistic and Deep Generative Models. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.618069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
We propose a method for multimodal concept formation. In this method, unsupervised multimodal clustering and cross-modal inference, as well as unsupervised representation learning, can be performed by integrating the multimodal latent Dirichlet allocation (MLDA)-based concept formation and variational autoencoder (VAE)-based feature extraction. Multimodal clustering, representation learning, and cross-modal inference are critical for robots to form multimodal concepts from sensory data. Various models have been proposed for concept formation. However, in previous studies, features were extracted using manually designed or pre-trained feature extractors and representation learning was not performed simultaneously. Moreover, the generative probabilities of the features extracted from the sensory data could be predicted, but the sensory data could not be predicted in the cross-modal inference. Therefore, a method that can perform clustering, feature learning, and cross-modal inference among multimodal sensory data is required for concept formation. To realize such a method, we extend the VAE to the multinomial VAE (MNVAE), the latent variables of which follow a multinomial distribution, and construct a model that integrates the MNVAE and MLDA. In the experiments, the multimodal information of the images and words acquired by a robot was classified using the integrated model. The results demonstrated that the integrated model can classify the multimodal information as accurately as the previous model despite the feature extractor learning in an unsupervised manner, suitable image features for clustering can be learned, and cross-modal inference from the words to images is possible.
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Taniguchi T, El Hafi L, Hagiwara Y, Taniguchi A, Shimada N, Nishiura T. Semiotically adaptive cognition: toward the realization of remotely-operated service robots for the new normal symbiotic society. Adv Robot 2021. [DOI: 10.1080/01691864.2021.1928552] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2022]
Affiliation(s)
- Tadahiro Taniguchi
- Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
| | - Lotfi El Hafi
- Ritsumeikan Global Innovation Research Organization, Ritsumeikan University, Kusatsu, Shiga, Japan
| | - Yoshinobu Hagiwara
- Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
| | - Akira Taniguchi
- Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
| | - Nobutaka Shimada
- Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
| | - Takanobu Nishiura
- Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
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