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DeepMind 设计了一个模仿人类两种系统思维的代理框架。

代理系统有望为开发应用程序和解决任务创造新范式。然而，人工智能代理解决的任务类型具有不同的复杂程度，需要不同的记忆和推理能力。

受到诺贝尔奖获得者丹尼尔·卡尼曼提出的双系统思维范式的启发，DeepMind 的研究人员开发了一种名为 Talker-Reasoner 的新代理框架。

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该框架和相关研究的前提是，当前的代理框架主要设计用于解决系统 1 任务，这些任务依赖于模式识别和即时记忆访问。当涉及到系统 2 任务时，代理系统就会崩溃，因为系统 2 任务需要更有条理的推理和规划。

Talker-Reasoner 通过将代理分为两个模块来解决这个问题：Talker 和 Reasoner。

两个模块通过共享内存系统进行交互。推理器用其最新的信念和推理结果更新内存。谈话器检索这些信息以做出快速决策。

研究人员在一款睡眠辅导应用中测试了他们的框架，该应用通过自然语言与用户互动，并提供个性化的指导和支持以改善睡眠习惯。这款应用需要结合快速、富有同理心的对话和深思熟虑的、基于知识的推理。同样的模式可以应用于其他类型的应用，包括个性化教育助理和客户服务。

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