Building a Cognitive Twin Using a Distributed Cognitive System and an Evolution Strategy

TL;DR

This paper introduces a method to create a Cognitive Twin by training a distributed cognitive system with an evolution strategy, enabling realistic human interaction modeling for automation and research.

Contribution

It demonstrates a novel approach combining distributed architectures and evolution strategies to build interaction-based Cognitive Twins from input-output data.

Findings

Effective approximation of human interaction behavior

Successful end-to-end training of distributed systems

Potential applications in automation and human-like agents

Abstract

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.