递归Metropolis-Hastings命名游戏：基于概率生成模型的多智能体系统中的符号涌现
Recursive Metropolis-Hastings Naming Game: Symbol Emergence in a Multi-agent System based on Probabilistic Generative Models
AI总结：
本文将Metropolis-Hastings命名游戏从双智能体扩展到N智能体场景，提出递归Metropolis-Hastings命名游戏（RMHNG），证明其是对共享潜在变量后验分布的去中心化近似贝叶斯推断，并通过合成与真实图像数据实验验证了多智能体符号涌现的有效性。
摘要：
在关于智能体群体中符号涌现与涌现通信的研究中，采用了一种计算模型，其中智能体参与各种语言游戏。在这些语言游戏中，Metropolis-Hastings命名游戏（MHNG）具有一个显著的数学性质：通过MHNG实现的符号涌现被证明是智能体共享表征的一种去中心化贝叶斯推断。然而，先前提出的MHNG仅限于双智能体场景。本文将MHNG扩展到N智能体场景。本文的主要贡献有两方面：（1）我们提出递归Metropolis-Hastings命名游戏（RMHNG）作为MHNG的N智能体版本，并证明RMHNG与MHNG类似，是对智能体共享的潜在变量后验分布的一种近似贝叶斯推断方法；（2）我们在合成图像数据和真实图像数据上实证评估了RMHNG的性能，使多个智能体能够发展并共享一个符号系统。此外，我们引入了两种近似——单样本近似和有限长度近似——以降低计算复杂度，同时保持解释智能体群体中通信的能力。实验结果表明，RMHNG作为一种去中心化贝叶斯推断方法，在近似智能体之间共同共享的潜在变量后验分布方面，具有与MHNG类似的有效性。此外，RMHNG的使用阐明了智能体交换符号的能力。研究还发现，即使是计算上简化版本的RMHNG也能使符号在智能体之间涌现。
英文摘要：
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, 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. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
