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The Mirror Agent Model: a Bayesian Architecture for Interpretable Agent Behavior

arXiv 2609.05190 ↗First posted 4 Sep 2026cs.AIcited by 0

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Abstract (arXiv)

In this paper we illustrate a novel architecture generating interpretable behavior and explanations. We refer to this architecture as the Mirror Agent Model because it defines the observer model, that is the target of explicit and implicit communications, as a mirror of the agent's. With the goal of providing a general understanding of this work, we firstly show prior relevant results addressing the informative communication of agents intentions and the production of legible behavior. In the second part of the paper we furnish the architecture with novel capabilities for explanations through off-the-shelf saliency methods, followed by preliminary qualitative results.

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References · 10

  1. 2016OpenAI Gymcited by 1,736
  2. 2018Techniques for Interpretable Machine Learningcited by 238
  3. 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 166
  4. 2017Visualizing and Understanding Atari Agentscited by 141
  5. 2019Towards Interpretable Reinforcement Learning Using Attention Augmented Agentscited by 82
  6. 2018Contrastive Explanation: A Structural-Model Approachcited by 65
  7. 2019Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learningcited by 60
  8. 2018Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behaviorcited by 30
  9. 2019Towards Explainable AI Planning as a Servicecited by 14
  10. 2021A Unifying Bayesian Formulation of Measures of Interpretability in Human-AIcited by 5

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