Classical Planning

Classical planning focuses on automatically finding sequences of actions to achieve a goal in a defined environment, a fundamental problem in artificial intelligence. Current research emphasizes improving the efficiency and scalability of planning algorithms, particularly through the integration of machine learning techniques like graph neural networks and reinforcement learning, and the use of large language models to bridge the gap between natural language problem descriptions and formal planning representations. These advancements are crucial for enabling more robust and adaptable autonomous systems in various applications, from robotics and logistics to game playing and even quantum computing circuit optimization.