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2025Ali Abdelghafour Bejaoui, Meriam Jemel, Nadia Ben Azzouna
Explainable AI Planning:literature review
Automated planning systems have become indispensable tools in a wide range of applications, from robotics and healthcare to logistics and autonomous systems. However, as these systems grow in complexity, their decision-making processes often become opaque, 2025
Résumé
Explainable AI Planning (XAIP) is a pivotal research
area focused on enhancing the transparency, interpretability,
and trustworthiness of automated planning systems. This
paper provides a comprehensive review of XAIP, emphasizing key
techniques for plan explanation, such as contrastive explanations,
hierarchical decomposition, and argumentative reasoning frameworks.
We explore the critical role of argumentation in justifying
planning decisions and address the challenges of replanning in
dynamic and uncertain environments, particularly in high-stakes
domains like healthcare, autonomous systems, and logistics.
Additionally, we discuss the ethical and practical implications
of deploying XAIP, highlighting the importance of human-AI
collaboration, regulatory compliance, and uncertainty handling.
By examining these aspects, this paper aims to provide a detailed
understanding of how XAIP can improve the transparency,
interpretability, and usability of AI planning systems across
various domains.
BibTeX
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systems,” Proceedings of the International Conference on Principles of
Knowledge Representation and Reasoning (KR), pp. 678–687, 2021.
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no. 2, pp. 123–150, 2021.
[20] T. Chakraborti, S. Sreedharan, Y. Zhang, and S. Kambhampati, “Plan
explanations as model reconciliation: Moving beyond explanation as
soliloquy,” in Proceedings of the International Joint Conference on
Artificial Intelligence (IJCAI), pp. 156–163, 2017.
[21] S. Sreedharan, T. Chakraborti, and S. Kambhampati, “Foundations of
explanations as model reconciliation,” Artificial Intelligence, vol. 281,
p. 103234, 2020.
[22] Y. Zhang, S. Sreedharan, and S. Kambhampati, “Interactive plan ex-
planations for human-ai collaboration,” in Proceedings of the AAAI
Conference on Artificial Intelligence (AAAI), pp. 8152–8159, 2019.
[23] S. L. Vasileiou and W. Yeoh, “Explainable planning for ethical ai
systems,” in Proceedings of the International Conference on Principles
of Knowledge Representation and Reasoning (KR), pp. 678–687, 2021.
[24] S. Kim, K. Lee, and N. Patel, “Machine learning techniques for
contrastive explanations in ai planning,” Journal of Artificial Intelligence
Research (JAIR), vol. 65, pp. 123–150, 2021.
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Springer, 2009.
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[27] A. Toniolo and T. J. Norman, “Argumentation-based decision making for
autonomous agents,” Artificial Intelligence Journal, vol. 232, pp. 1–25,
2015.
[28] M. Fox and D. Long, “Pddl2.1: An extension to pddl for expressing
temporal planning domains,” Journal of Artificial Intelligence Research,
vol. 20, pp. 61–124, 2003.
[29] X. Fan and F. Toni, “Explanations in automated planning: Argumentation
and interactive justifications,” Artificial Intelligence, vol. 283, pp. 103–
113, 2020.
[30] S. Heras and S. Villata, “Explainable argumentation for human-ai col-
laboration,” Journal of Artificial Intelligence Research, vol. 67, pp. 151–
177, 2018.
[31] M. Caminada and M. Podlaszewski, “Natural language generation for
argumentation-based explainability,” Computational Models of Argu-
ment, pp. 209–220, 2020.
[32] S. Bistarelli and F. Santini, “Complexity considerations in structured
argumentation,” Artificial Intelligence Review, vol. 55, pp. 213–234,
2022.
[33] L. Cohen and S. Modgil, “Justification-based argumentation for explain-
ability in ai systems,” Knowledge-Based Systems, vol. 227, pp. 107–124,
2021.
[34] I. Rahwan and G. R. Simari, “Argumentation in artificial intelligence,”
Springer, 2009.
[35] S. Modgil and M. Caminada, “A general account of argumentation with
preferences,” Artificial Intelligence, vol. 217, pp. 1–42, 2014.
[36] A. Toniolo and T. J. Norman, “Argumentation-based decision making for
autonomous agents,” Artificial Intelligence Journal, vol. 232, pp. 1–25,
2015.
[37] M. Fox and D. Long, “Pddl2.1: An extension to pddl for expressing
temporal planning domains,” Journal of Artificial Intelligence Research,
vol. 20, pp. 61–124, 2003.
[38] X. Fan and F. Toni, “Explanations in automated planning: Argumentation
and interactive justifications,” Artificial Intelligence, vol. 283, pp. 103–
113, 2020.
[39] S. Heras and S. Villata, “Explainable argumentation for human-ai col-
laboration,” Journal of Artificial Intelligence Research, vol. 67, pp. 151–
177, 2018.
[40] M. Caminada and M. Podlaszewski, “Natural language generation for
argumentation-based explainability,” Computational Models of Argu-
ment, pp. 209–220, 2020.
[41] S. Bistarelli and F. Santini, “Complexity considerations in structured
argumentation,” Artificial Intelligence Review, vol. 55, pp. 213–234,
2022.
[42] L. Cohen and S. Modgil, “Justification-based argumentation for explain-
ability in ai systems,” Knowledge-Based Systems, vol. 227, pp. 107–124,
2021.