# Towards Explainable AI Planning as a Service

arXiv: https://arxiv.org/abs/1908.05059 · HTML: https://mlsanity.com/paper/1908.05059/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 14 of 14)

- [Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey](https://mlsanity.com/paper/2108.09003/) (2021)
- [Seven challenges for harmonizing explainability requirements](https://mlsanity.com/paper/2108.05390/) (2021)
- [Policy Regularization for Legible Behavior](https://mlsanity.com/paper/2203.04303/) (2022)
- [Explainable Goal Recognition: A Framework Based on Weight of Evidence](https://mlsanity.com/paper/2303.05622/) (2023)
- [Rule-based Shielding for Partially Observable Monte-Carlo Planning](https://mlsanity.com/paper/2104.13791/) (2021)
- [On Explainability in AI-Solutions: A Cross-Domain Survey](https://mlsanity.com/paper/2210.05173/) (2022)
- [Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs](https://mlsanity.com/paper/2604.21092/) (2026)
- [Actions You Can Handle: Dependent Types for AI Plans](https://mlsanity.com/paper/2105.11267/) (2021)
- [Towards Probabilistic Planning of Explanations for Robot Navigation](https://mlsanity.com/paper/2411.05022/) (2024)
- [Counterfactual Explanations as Plans](https://mlsanity.com/paper/2502.09205/) (2025)
- [What Questions Should Robots Be Able to Answer? A Dataset of User Questions for Explainable Robotics](https://mlsanity.com/paper/2510.16435/) (2025)
- [Informative Communication of Robot Plans](https://mlsanity.com/paper/2511.13226/) (2025)
- [Contrastive explanations of BDI agents](https://mlsanity.com/paper/2602.13323/) (2026)
- [The Mirror Agent Model: a Bayesian Architecture for Interpretable Agent Behavior](https://mlsanity.com/paper/2609.05190/) (2026)

## Abstract (arXiv)

Explainable AI is an important area of research within which Explainable Planning is an emerging topic. In this paper, we argue that Explainable Planning can be designed as a service -- that is, as a wrapper around an existing planning system that utilises the existing planner to assist in answering contrastive questions. We introduce a prototype framework to facilitate this, along with some examples of how a planner can be used to address certain types of contrastive questions. We discuss the main advantages and limitations of such an approach and we identify open questions for Explainable Planning as a service that identify several possible research directions.