Building a Cognitive Twin using a distributed cognitive system and an evolution strategy

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 is 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 endto-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.