Domain Independent

Domain-independent planning aims to create algorithms and systems capable of solving diverse planning problems without requiring problem-specific modifications. Current research focuses on improving the efficiency and robustness of these systems, exploring techniques like contrastive explanations to enhance user understanding, learning safe action models from observations, and integrating large language models for natural language interfaces and problem decomposition. This work is significant because it strives to create more general-purpose and user-friendly AI planning tools applicable across various domains, from smart home energy management to robotics and puzzle solving.