Generalized Planning

Generalized planning (GP) aims to create algorithms that solve multiple related planning problems, unlike classical planning which addresses single instances. Current research focuses on improving the efficiency and scalability of GP algorithms, particularly through heuristic search methods like best-first search and the incorporation of techniques such as landmarks and parallel processing. Prominent approaches leverage graph neural networks and program synthesis (e.g., using C++ or Python) to represent and learn generalizable solutions, often incorporating techniques from reinforcement learning. These advancements hold significant potential for improving the efficiency and robustness of AI planning in various applications, including robotics and resource allocation.