Design and Evaluation of Explainable BDI Agents

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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “• Interactivity: Some contributions [50,59] include the ability of a model to be interactive with the user as one of the goals targeted by an explainable ML model.”
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, background): “Significant earlier works proposed approaches for self-explaining virtual agents in scenario-based training systems [173], [174].”
Explainable Agents and Robots: Results from a Systematic Literature Review (2019, methodology): “This technique has been used in modeling agent’s goals, beliefs and actions hierarchy [39].”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “[337, 338] showed that users of simulation-based training systems with virtual players prefer short explanations to long ones where length is defined by the number of elements in an explanation.”
The role of emotion in self-explanations by cognitive agents (2017, background): “Another approach focusses on the generation of explanations from beliefs and desires [4, 15].”
Formalizing Explanatory Dialogues (2015, background): “Many work have proposed architectures and models [12,8,10] to incorporate explanation within agent’s design for various reasons (i.”