GOFAI Based Planning

GOFAI-based planning focuses on developing and improving classical AI planning techniques for complex tasks, particularly in large-scale or dynamic environments. Current research emphasizes enhancing efficiency through methods like leveraging large language models (LLMs) to prune search spaces and learning action costs from observed plans, alongside exploring the interpretability of LLM-based planning mechanisms. These advancements aim to improve the robustness and applicability of GOFAI planning in real-world scenarios, such as robotics and autonomous vehicle navigation, by addressing challenges like computational complexity and the need for accurate, efficiently learned models of action.