用进化策略训练分布式认知系统，构建可模仿人类交互行为的数字分身。
Building a Cognitive Twin Using a Distributed Cognitive System and an Evolution Strategy
- 基于分布式认知架构，通过输入输出训练和进化策略端到端构建认知孪生体。
- 多设备协同实现对个体交互行为的良好逼近，具备自动化任务与生成类人智能体潜力。
- 适合用于人机交互研究、数字分身开发及行为模拟场景。
本文提出一种基于分布式认知架构框架，结合输入-输出训练与进化策略，构建交互式认知孪生体（Cognitive Twin）的技术。通过端到端训练，将多个物理与虚拟设备协同整合，实现对个体交互行为的高精度近似。实验展示了系统的性能指标，验证了其在模拟真实人类行为方面的可行性。生成的认知孪生体可用于自动化任务执行、创建更逼真的类人智能体，或进一步探究其行为模式。
原文摘要 · Abstract (English)
This work presents a technique to build interaction-based Cognitive Twins (a computational version of an external agent) using input-output training and an Evolution Strategy on top of a framework for distributed Cognitive Architectures. Here, we show that it's possible to orchestrate many simple physical and virtual devices to achieve good approximations of a person's interaction behavior by training the system in an end-to-end fashion and present performance metrics. The generated Cognitive Twin may later be used to automate tasks, generate more realistic human-like artificial agents or further investigate its behaviors.
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