# An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems

arXiv: https://arxiv.org/abs/2309.14391 · HTML: https://mlsanity.com/paper/2309.14391/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 44 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 16 of 16)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Explainable Reinforcement Learning: A Survey](https://mlsanity.com/paper/2005.06247/) (2020)
- [Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models](https://mlsanity.com/paper/2208.07852/) (2022)
- [Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations](https://mlsanity.com/paper/1912.09007/) (2019)
- [A Grounded Interaction Protocol for Explainable Artificial Intelligence](https://mlsanity.com/paper/1903.02409/) (2019)
- [From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent](https://mlsanity.com/paper/2209.02552/) (2022)
- [What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations](https://mlsanity.com/paper/2002.05674/) (2020)
- [Software-Based Dialogue Systems: Survey, Taxonomy and Challenges](https://mlsanity.com/paper/2106.10901/) (2021)
- [Feature-Model-Guided Online Learning for Self-Adaptive Systems](https://mlsanity.com/paper/1907.09158/) (2019)
- [Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems](https://mlsanity.com/paper/2210.05931/) (2022)
- [AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions](https://mlsanity.com/paper/2212.04645/) (2022)
- [A User Study on Explainable Online Reinforcement Learning for Adaptive Systems](https://mlsanity.com/paper/2307.04098/) (2023)

## Built on by (most cited first, 1 of 1)

- [XABPs: Towards eXplainable Autonomous Business Processes](https://mlsanity.com/paper/2507.23269/) (2025)

## Abstract (arXiv)

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.