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递归大都会 - 黑斯廷斯命名游戏:基于概率性的生成模型的多代理系统中的符号出现
Jun Inukai1, Tadahiro Taniguchi1, Akira Taniguchi1
1Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan.
Frontiers in artificial intelligence
|November 3, 2023
概括
本研究介绍了复制性大都会-哈斯廷斯命名游戏 (RMHNG) 用于多代理符号的出现. RMHNG能够实现分散的贝叶斯推理共享表示,促进群体的通信和符号系统.
科学领域:
- 计算语言学计算语言学
- 人工智能的人工智能是人工智能.
- 基于代理的建模模型.
背景情况:
- 符号的出现和通信是多代理系统的关键.
- 大都会 - 黑斯廷斯命名游戏 (MHNG) 将此作为分散的贝叶斯推理模型,但仅限于两个代理.
- 将MHNG扩展到N-代理情景对于理解人口层面的沟通至关重要.
研究的目的:
- 提出和评估N-代理符号出现的递归大都市-海斯廷斯命名游戏 (RMHNG).
- 为了证明RMHNG作为共享潜变量的近似贝叶斯推理方法的能力.
- 评估RMHNG在合成和现实世界的图像数据上的表现.
主要方法:
- 开发用于N个代理的递归大都市-海斯廷斯命名游戏 (RMHNG).
- 使用合成数据和YCB对象数据集的实证评估.
- 引入单样和有限长度近似来减少计算复杂性.
主要成果:
- 在N-代理系统中,RMHNG有效地执行分散的贝叶斯推理,用于共享的潜在变量.
- 该模型使多个代理开发和共享一个符号系统.
- 接近版本的RMHNG保持能力,以减少复杂性来解释符号的出现.
- 虽然有效,但ARI和kappa的改善在真实图像数据上较小.
结论:
- RMHNG是MHNG的可行扩展,用于N-代理符号的出现和通信.
- 该模型为代理人之间的共享表示提供了分散的贝叶斯推理.
- 对RMHNG的计算简化仍然允许在代理群体中有效的符号出现.
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