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.

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.

Artificial intelligence (AI) hierarchical planning usually entails the following essential elements:

• High-Level Goals: High-level goals provide the initial direction for the planning process and guide the decomposition of tasks into smaller sub-goals.
• Tasks: Tasks are actions that need to be performed to accomplish the high-level goals.
• Sub-Goals: Sub-goals are intermediate objectives that contribute to the accomplishment of higher-level goals. Sub-goals are derived from decomposing high-level goals into smaller, more manageable tasks.
• Hierarchical Structure: Hierarchical planning organizes tasks and goals into a hierarchical structure, where higher-level goals are decomposed into sub-goals, and sub-goals are further decomposed until reaching primitive actions that can be directly executed.
• Task Dependencies and Constraints: Hierarchical planning considers dependencies and constraints between tasks and sub-goals. These dependencies determine the order in which tasks need to be executed and any preconditions that must be satisfied before a task can be performed.
• Plan Representation: Plans in hierarchical planning are represented as hierarchical structures that capture the sequence of tasks and sub-goals required to achieve the high-level goals. This representation facilitates efficient plan generation, execution, and monitoring.
• Plan Evaluation and Optimization: Hierarchical planning involves evaluating and optimizing plans to ensure they meet the desired criteria, such as efficiency, feasibility, and resource utilization. This may involve iteratively refining the plan structure or adjusting task priorities to improve performance.

In section, we are going to discuss the hierarchical planning techniques that are leveraged for organizing and executing hierarchical structures:

1. HTN (Hierarchical Task Network) Planning

HTN planning decomposing high-level tasks into simpler sub-tasks using hierarchical structures called task networks. HTN planning enables the representation of complex tasks as hierarchical networks of actions and conditions, allowing for flexible and modular planning.

2. Hierarchical Reinforcement Learning (HRL)

HRL is extension of reinforcement learning, it leverages hierarchical structures to facilitate learning and decision-making in complex environments. In HRL, tasks are organized into a hierarchy of sub-goals, and the agent learns policies for achieving these sub-goals at different levels of abstraction. By learning hierarchies of policies, HRL enables more efficient exploration and exploitation of the environment, leading to faster learning and improved performance.

3. Hierarchical Task Networks (HTNs)

HTNs are used for representing and reasoning about hierarchical task decomposition. HTNs consist of a set of tasks organized into a hierarchy, where higher-level tasks are decomposed into sequences of lower-level tasks. HTNs provide a structured framework for planning and execution, allowing for the efficient generation of plans that satisfy complex goals and constraints.

4. Hierarchical State Space Search

Hierarchical state space search is a planning technique that involves exploring the state space of a problem in a hierarchical manner. Instead of directly exploring individual states, hierarchical state space search organizes states into hierarchical structures, where higher-level states represent abstract representations of the problem space. This hierarchical exploration allows for more efficient search and pruning of the state space, leading to faster convergence and improved scalability.

Let’s consider an example of autonomous driving car, here hierarchical planning is employed in the following manner:

1. High-Level Goal: safely navigate from A to B, following the traffic rules
2. Major Steps:
   1. Route Planning: determine optimal route to B
   2. Obstacle Avoidance: identify obstacles like vehicle, people, etc.
   3. Traffic Signal Recognition: detect traffic signals and signs
   4. Lane Keeping: stay in the designated lane and adjust the vehicle’s position to avoid collision
3. Minor Steps:
   1. Route Planning:
      • Map Analysis: analyze maps to find the optimal route
      • Traffic Prediction: predict traffic patterns to avoid traffic jams.
   2. Obstacle Avoidance:
      • Sensor Data Processing: process the data from onboard sensor to detect nearby objects
      • Path Planning: generate paths to avoid obstacles
   3. Traffic Signal Recognition:
      • Image Recognition: analyze images to detect traffic lights
      • Traffic Rule Interpretation: interpret and detect signal to determine the action
   4. Lane Keeping:
      • Lane Detection: use computer vision algorithms to detect lane markings
      • Control Systems: adjust the speed, steering, break command to keep the vehicle within the detected lane.
4. Hierarchical Planning:
   1. First Level Plan: Define the high-level goals and major steps, such as route planning, obstacle avoidance, traffic signal recognition, and lane keeping.
   2. Second Level Plan: Break down each major step into subtasks and minor steps, as described above, to handle the complexity of each component.
   3. Third Level Plan: Further decompose the minor steps into detailed actions and algorithms necessary to execute them effectively.

Hierarchical Planning Techniques in Autonomous Driving

In autonomous driving, hierarchical planning techniques are crucial for safe navigation.

• HTN Planning: Decomposes route planning into subtasks like map analysis and traffic prediction, ensuring optimal routes.
• Hierarchical Reinforcement Learning (HRL): Learns hierarchical policies for obstacle avoidance, adjusting vehicle trajectory to avoid collisions.
• Hierarchical Task Networks (HTNs): Decomposes traffic signal recognition into subtasks for accurate detection and rule interpretation.
• Hierarchical State Space Search: Explores state space of lane keeping, adjusting vehicle commands for effective lane-keeping strategies.

Let’s consider an example of hierarchical planning applied to a robotic arm tasked with assembling electronic devices:

1. High-Level Goal: ensemble electronic devices following the provided assembly process
2. Major Steps:
   1. Identifying Components: identify and local the components required for assembling the device.
   2. Planning Assembly Sequence: determine optimal sequence of assembly steps to minimize assembly time and maximize efficiency.
   3. Manipulation and Grasping: manipulate the arm of the robot to grasp and manipulate the electronic components.
   4. Quality Control: ensure the quality of assembly step and detect and correct any errors.
3. Minor Steps:
   1. Identifying Components:
      1. Object Recognition: recognize components using computer vision
      2. Inventory Management: maintain inventory of the components
   2. Planning Assembly Sequence:
      1. Task Planning: break the process into smaller and sequential tasks
      2. Motion Planning: planning the arm movement to perform assembling
   3. Manipulation and Grasping:
      1. Grasping strategy: determine optimal grasping pose based on shape and side of the components.
      2. Path Generation: generate smooth trajectories for the robotic arm
   4. Quality Control:
      1. Vision Inspection: inspect assembled components using cameras
      2. Feedback Control: implement feedback mechanism to adjust assembly actions.
4. Hierarchical Planning:
   1. First Level Plan: Define the high-level goals and major steps, including component identification, assembly sequence planning, manipulation and grasping, and quality control.
   2. Second Level Plan: Break down each major step into subtasks and minor steps, such as object recognition, inventory management, task planning, motion planning, grasping strategy, path generation, vision inspection, and feedback control.
   3. Third Level Plan: Further decompose the minor steps into detailed actions and algorithms necessary to execute them effectively, such as specific image processing algorithms for object recognition or control algorithms for adjusting robotic arm movements based on inspection results.

Hierarchical Planning Techniques in Robotics

In robotic arm assembly, hierarchical planning techniques like HTN planning, hierarchical reinforcement learning (HRL), HTNs, and hierarchical state space search ensure efficient execution.

• HTN Planning: Organizes major steps hierarchically, facilitating task decomposition.
• Hierarchical Reinforcement Learning (HRL): Learns hierarchical policies for manipulation and grasping, optimizing performance.
• HTNs (Hierarchical Task Networks): Structures minor steps systematically, ensuring accurate component handling.
• Hierarchical State Space Search: Optimizes assembly sequences considering constraints, ensuring efficiency.

Here, the advantages of hierarchical planning:

• Users capability: Planning and reasoning at various levels of abstraction are made possible by hierarchical planning, which makes it possible to handle complicated tasks and situations effectively.
• Internal Flexibility: Plans can be adjusted to reflect changes in the environment or goals thanks to the flexibility that hierarchical planning offers, which strengthens and adapts the plans.
• Personal reuse and abstraction: By employing a hierarchy of activities or sub goals, plans can be repurposed and abstracted, increasing planning effectiveness and decreasing the need for duplicate planning.
• Higher-level reasoning addaptibility: AI systems are able to make strategic decisions and coordinate actions at a higher degree of abstraction thanks to hierarchical planning, which facilitates higher-level reasoning and decision-making.

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 is an essential strategy used in AI system. This planning technique is particularly helpful when dealing with issues that are too complicated to tackle. Hierarchal planning enables dynamic dynamic modification of plans at various levels of abstraction, it promotes adaptation and flexibility in dynamic contexts. Integrating machine learning techniques with it can improve the decision-making capabilities of AI systems.

What do you mean by hierarchy planning?

The organisational levels and units in your business that you wish to plan for are represented by a planning hierarchy. Combining characteristic values from several information structures results in a planning hierarchy.

What do you mean by hierarchical level of AI?

Hierarchical models in AI incorporate a structured approach to expressing and analyzing complicated relationships and patterns within data. These models are aimed to capture the hierarchical character of real-world occurrences, enabling multi-level representations and meaningful analysis.

How many types of hierarchy present in AI?

The subsumptive containment hierarchy and the compositional containment hierarchy are the two different forms of containment hierarchies. Whereas a compositional hierarchy is “composed” of its children, a subsumptive hierarchy “subsumes” its offspring.