Keywords

Reflective System, Knowledge Engineering (KE), eXplainable Artificial Intelligence (XAI)

Reflective Systems & Knowledge Engineering

Reflective Systems can be considered as sets of interacting entities (including humans) that benefit from means for supporting their Co-evolution. These means are designed to help actors in analyzing (and eventually adapting) the system's behaviour. The general objective is to facilitate the system's understanding and evolution.

One of the main challenges in creating reflective systems is to provide reflective functionalities that are adapted to the actor's abstraction level(s) and viewpoint(s). Those are closely related to the actor's knowledge.

SysReIC's research focuses on methods and tools in Knowledge Engineering for creating Reflective Systems.

Explainable AI (XAI)

A crucial research question in Artificial Intelligence is how to create XAI (eXplainable AI).
This question arose from the wide spread of Machine Learning in user's everyday life, and has been triggered from the accompanying need for trust regarding fairness, robustness, safety, etc.
From our point of view, the challenge of XAI can be considered as a matter of creating AI Reflective Systems.

SysReIC aims at creating eXplainable AI by marrying Machine Learning and Knowledge Engineering.