Hierarchical Planning is crucial in both AI and robotics for dealing with complex tasks that can be decomposed into smaller, more manageable components. This approach leverages a hierarchical structure to simplify decision-making processes, where higher levels of the hierarchy define broader goals and lower levels focus on detailed actions required to achieve these goals. In robotics, this means breaking down a complex task like navigating an environment into sub-tasks such as path planning, obstacle avoidance, and motion control. Hierarchical Planning allows for more efficient problem-solving as it reduces the search space for solutions at each level of the hierarchy and facilitates the reuse of solutions for common sub-problems, thereby improving the scalability and efficiency of the planning process.
Historical overview: The concept of Hierarchical Planning has been part of AI and robotics since the early days, but it gained significant attention in the 1970s with the development of the STRIPS planner and its hierarchical extension, ABSTRIPS, which demonstrated the efficiency of hierarchical approaches in planning.
Key contributors: The development of ABSTRIPS by Sacerdoti in the mid-1970s played a pivotal role in demonstrating the effectiveness of hierarchical planning. This method and its successors have influenced numerous domains within AI, particularly in robotics and complex system management, highlighting its foundational importance in the field.