What is Hierarchical Planning in AI?
In artificial intelligence (AI), hierarchical planning is a planning methodology that entails grouping tasks and actions into several abstraction levels or hierarchies, with higher-level jobs being broken down into a series of lower-level tasks. It offers a method for effectively using a hierarchy of goals and sub-goals to reason and plan in complex contexts.
AI systems can effectively handle complicated tasks and surroundings because of hierarchical planning, which enables them to make decisions at many levels of abstraction. Compared to flat planning systems, which treat tasks at the same level of abstraction, this approach differs. AI systems can efficiently handle relationships, prioritize tasks, and distribute resources thanks to the structured method of hierarchical planning, which makes it very useful in complicated contexts.
Hierarchical Planning in AI
Hierarchical Planning in Artificial Intelligence is a problem-solving and decision-making technique employed to reduce the computational expense associated with planning. The article provides an overview of hierarchical planning in AI, discussing its components, techniques, applications in autonomous driving and robotics, advantages, and challenges.
Table of Content
- What is Hierarchical Planning in AI?
- Components of Hierarchical Planning
- Hierarchical Planning Techniques in AI
- 1. HTN (Hierarchical Task Network) Planning
- 2. Hierarchical Reinforcement Learning (HRL)
- 3. Hierarchical Task Networks (HTNs)
- 4. Hierarchical State Space Search
- Hierarchical Planning in Autonomous Driving
- Hierarchical Planning in Robotics
- Advantages of Hierarchical Planning
- Hierarchical Planning in AI – FAQs