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'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.

Neurosymbolic AI and its Taxonomy: a survey

Neurosymbolic AI deals with models that combine symbolic processing, like classic AI, and neural networks, as it's a very established area. These models are emerging as an effort toward Artificial General Intelligence (AGI) by both exploring an alternative to just increasing datasets' and models' sizes and combining Learning over the data distribution, Reasoning on prior and learned knowledge, and by symbiotically using them. This survey investigates research papers in this area during recent years and brings classification and comparison between the presented models as well as applications.