Explainable Planning for Hybrid Systems with AI

The recent advancement in artificial intelligence (AI) technologies has facilitated a paradigm shift toward automation. Autonomous systems are fully or partially replacing manually crafted ones. At the core of these systems is automated planning. With the increasing complexity of these systems, explainability has become a crucial aspect of planning. Explainable planning for hybrid systems aims to provide insights into the decision-making process of AI-driven systems. This enables humans to understand and trust the recommendations made by these systems. The concept of hybrid systems is gaining traction in various industries, including transportation and healthcare, where AI is being integrated with human oversight to improve safety and efficiency. The integration of AI and human oversight provides a more robust and reliable decision-making system. However, the lack of explainability in AI-driven decision-making has been a significant barrier to widespread adoption. The new explainable planning approach addresses this challenge by providing transparent and interpretable results. This innovation has significant implications for various sectors, including transportation and healthcare, where the ability to understand and trust AI-driven decision-making is crucial.