Explainable AI Planning:literature review

Abstract

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.

Ähnliche Arbeiten