mlsanityData to 7 Oct 2026

Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior

arXiv 1811.09722 ↗First posted 23 Nov 2018cs.AIcited by 30

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Abstract (arXiv)

There has been significant interest of late in generating behavior of agents that is interpretable to the human (observer) in the loop. However, the work in this area has typically lacked coherence on the topic, with proposed solutions for "explicable", "legible", "predictable" and "transparent" planning with overlapping, and sometimes conflicting, semantics all aimed at some notion of understanding what intentions the observer will ascribe to an agent by observing its behavior. This is also true for the recent works on "security" and "privacy" of plans which are also trying to answer the same question, but from the opposite point of view -- i.e. when the agent is trying to hide instead of revealing its intentions. This paper attempts to provide a workable taxonomy of relevant concepts in this exciting and emerging field of inquiry.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Nov 2018.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 7; 2022: 5; 2023: 5; 2024: 4; 2025: 5; 2026: 3.

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  1. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 123
  2. 2021Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recoverycited by 32
  3. 2024Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion?cited by 22
  4. 2021Explainable Autonomous Robots: A Survey and Perspectivecited by 15
  5. 2021Deceptive Reinforcement Learning for Privacy-Preserving Planningcited by 14
  6. 2021Human-AI Symbiosis: A Survey of Current Approachescited by 8
  7. 2021Definitions of intent suitable for algorithmscited by 8
  8. 2019Motion Reasoning for Goal-Based Imitation Learningcited by 6
  9. 2021A Unifying Bayesian Formulation of Measures of Interpretability in Human-AIcited by 5
  10. 2022Explaining Preference-driven Schedules: the EXPRES Frameworkcited by 5
  11. 2022A Mental-Model Centric Landscape of Human-AI Symbiosiscited by 3
  12. 2022Policy Regularization for Legible Behaviorcited by 3
18 more
  1. 2021Generating Active Explicable Plans in Human-Robot Teamingcited by 2
  2. 2022Causal Explanations for Sequential Decision Making Under Uncertaintycited by 2
  3. 2022"Guess what I'm doing": Extending legibility to sequential decision taskscited by 2
  4. 2023Planning for Attacker Entrapment in Adversarial Settingscited by 1
  5. 2023Safe Explicable Planningcited by 1
  6. 2024On the Transit Obfuscation Problemcited by 1
  7. 2024Generalising Planning Environment Redesigncited by 1
  8. 2024How to Exhibit More Predictable Behaviorscited by 1
  9. 2023A More General Theory of Diagnosis from First Principlescited by 0
  10. 2023ICRA Roboethics Challenge 2023: Intelligent Disobedience in an Elderly Care Homecited by 0
  11. 2025Observer-Aware Probabilistic Planning Under Partial Observabilitycited by 0
  12. 2025Safe Explicable Policy Searchcited by 0
  13. 2025Preprint: Exploring Inevitable Waypoints for Unsolvability Explanation in Hybrid Planning Problemscited by 0
  14. 2025Controlling Intent Expressiveness in Robot Motion with Diffusion Modelscited by 0
  15. 2025Informative Communication of Robot Planscited by 0
  16. 2026Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localizationcited by 0
  17. 2026The Mirror Agent Model: a Bayesian Architecture for Interpretable Agent Behaviorcited by 0
  18. 2026Probabilistic Plan Legibility with Off-the-shelf Plannerscited by 0