An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of
  Service-oriented Systems
Andreas Metzger Jone Bartel Jan Laufer
Deep Reinforcement Learning (Deep RL) is increasingly used to cope with the
open-world assumption in service-oriented systems. Deep RL was successfully
applied to problems such as dynamic service composition, job scheduling, and
offloading, as well as service adaptation. While Deep RL offers many benefits,
understanding the decision-making of Deep RL is challenging because its learned
decision-making policy essentially appears as a black box. Yet, understanding
the decision-making of Deep RL is key to help service developers perform
debugging, support service providers to comply with relevant legal frameworks,
and facilitate service users to build trust. We introduce Chat4XAI to
facilitate the understanding of the decision-making of Deep RL by providing
natural-language explanations. Compared with visual explanations, the reported
benefits of natural-language explanations include better understandability for
non-technical users, increased user acceptance and trust, as well as more
efficient explanations. Chat4XAI leverages modern AI chatbot technology and
dedicated prompt engineering. Compared to earlier work on natural-language
explanations using classical software-based dialogue systems, using an AI
chatbot eliminates the need for eliciting and defining potential questions and
answers up-front. We prototypically realize Chat4XAI using OpenAI's ChatGPT API
and evaluate the fidelity and stability of its explanations using an adaptive
service exemplar.