Challenges and Limitations of Hierarchical Planning
Although hierarchical planning has many benefits, there are some challenges and limitations as well:
- Planning Complexity: As the number of tasks rises, both the initial decomposition and the following planning may become computationally demanding.
- Adaptability: Modifications to the environment or the main objectives may call for a thorough re-planning process that may demand a large amount of resources.
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
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