一个框架,用于语言的出现和分析在社会学习代理人的语言
Tobias J Wieczorek1,2, Tatjana Tchumatchenko2, Carlos Wert-Carvajal3
1Department of Computer Science, Technical University Darmstadt, Darmstadt, Germany.
Nature communications
|August 31, 2024
概括
神经通信涉及内部表征和社会传播. 在代理之间优化消息内容可以提高任务性能和概括性,突出语言.
科学领域:
- 计算神经科学是一种计算神经科学.
- 人工智能的人工智能是人工智能.
- 认知科学是一种认知科学.
背景情况:
- 神经系统创建内部表示来导航环境.
- 社会约束迫使个人之间交流这些表征.
- 了解共享抽象是有效的代理间信息传输的关键.
研究的目的:
- 研究神经系统中内部表示的结构.
- 分析这些表示是如何优化为代理之间的信息传输.
- 检查共享抽象对任务执行和概括的影响.
主要方法:
- 在网格世界迷宫中利用了教师-学生强化学习的学习框架.
- 将高维任务信息压缩成一个低维的表示空间.
- 优化消息内容为学生奖励,以改善信息编码.
主要成果:
- 教师的信息显著提高了学生任务完成率和对未见过的任务的概括.
- 优化消息内容增强了信息编码,证明了对双向输入的需求.
- 建立了环境变量与个人/共享表示之间的联系.
结论:
- 语言作为一种关键的共同表征,促进了代理人之间的沟通.
- 通过优化消息有效的信息共享增强了概括能力.
- 双向输入对于通信空间内的准确表示至关重要.
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