mlsanityData to 7 Oct 2026

Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios

arXiv 2207.03214 ↗First posted 7 Jul 2022cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

Explainable artificial intelligence is a research field that tries to provide more transparency for autonomous intelligent systems. Explainability has been used, particularly in reinforcement learning and robotic scenarios, to better understand the robot decision-making process. Previous work, however, has been widely focused on providing technical explanations that can be better understood by AI practitioners than non-expert end-users. In this work, we make use of human-like explanations built from the probability of success to complete the goal that an autonomous robot shows after performing an action. These explanations are intended to be understood by people who have no or very little experience with artificial intelligence methods. This paper presents a user trial to study whether these explanations that focus on the probability an action has of succeeding in its goal constitute a suitable explanation for non-expert end-users. The results obtained show that non-expert participants rate robot explanations that focus on the probability of success higher and with less variance than technical explanations generated from Q-values, and also favor counterfactual explanations over standalone explanations.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 16 of its 41 references that are in the map. First posted on arXiv 7 Jul 2022.

Builds on

16 of 41 references.

References · 16

  1. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  2. 2018Metrics for Explainable AI: Challenges and Prospectscited by 257
  3. 2018Programmatically Interpretable Reinforcement Learningcited by 192
  4. 2019Explainable Reinforcement Learning Through a Causal Lenscited by 177
  5. 2017Visualizing and Understanding Atari Agentscited by 141
  6. 2020Explainable Reinforcement Learning: A Surveycited by 139
  7. 2017Interpretable Policies for Reinforcement Learning by Genetic Programmingcited by 47
  8. 2020The Emerging Landscape of Explainable AI Planning and Decision Makingcited by 44
  9. 2017Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanationscited by 37
  10. 2021Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Surveycited by 24
  11. 2021Levels of explainable artificial intelligence for human-aligned conversational explanationscited by 21
  12. 2020Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenariocited by 19
4 more
  1. 2020Distal Explanations for Model-free Explainable Reinforcement Learningcited by 12
  2. 2020Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environmentcited by 10
  3. 2020A Comparison of Humanoid Robot Simulators: A Quantitative Approachcited by 8
  4. 2020Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agentscited by 4

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 2.

5 papers.

  1. 2023Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposalcited by 1
  2. 2022Introspection-based Explainable Reinforcement Learning in Episodic and Non-episodic Scenarioscited by 0
  3. 2022Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenariocited by 0
  4. 2025Trust Through Transparency: Explainable Social Navigation for Autonomous Mobile Robots via Vision-Language Modelscited by 0
  5. 2025Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robotscited by 0