2025

Conférence

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

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.

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