# Effective Explanations for Belief-Desire-Intention Robots: When and What to Explain

arXiv: https://arxiv.org/abs/2507.02016 · HTML: https://mlsanity.com/paper/2507.02016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery](https://mlsanity.com/paper/2101.01625/) (2021)
- [Explainable Goal-Driven Agents and Robots -- A Comprehensive Review](https://mlsanity.com/paper/2004.09705/) (2020)
- [Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks](https://mlsanity.com/paper/2007.12803/) (2020)
- [Explainable Autonomous Robots: A Survey and Perspective](https://mlsanity.com/paper/2105.02658/) (2021)
- [Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs](https://mlsanity.com/paper/2004.12248/) (2020)

## Abstract (arXiv)

When robots perform complex and context-dependent tasks in our daily lives, deviations from expectations can confuse users. Explanations of the robot's reasoning process can help users to understand the robot intentions. However, when to provide explanations and what they contain are important to avoid user annoyance. We have investigated user preferences for explanation demand and content for a robot that helps with daily cleaning tasks in a kitchen. Our results show that users want explanations in surprising situations and prefer concise explanations that clearly state the intention behind the confusing action and the contextual factors that were relevant to this decision. Based on these findings, we propose two algorithms to identify surprising actions and to construct effective explanations for Belief-Desire-Intention (BDI) robots. Our algorithms can be easily integrated in the BDI reasoning process and pave the way for better human-robot interaction with context- and user-specific explanations.