Building a Cognitive Twin Using a Distributed Cognitive System and an Evolution Strategy
This addresses the need for more realistic human-like artificial agents or task automation, but it appears incremental as it builds on existing distributed cognitive frameworks.
The paper tackles the problem of creating a computational version of an external agent, called a Cognitive Twin, by orchestrating simple devices and training with an Evolution Strategy, achieving good approximations of a person's interaction behavior as shown by performance metrics.
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.