Hierarchical Task Networks (HTNs) for Agent Decision-Making
Hierarchical Task Networks (HTNs) are a powerful planning technique used in Artificial Intelligence, particularly for complex tasks that can be broken down into smaller, more manageable sub-tasks. They are essential for developing intelligent agents capable of sophisticated decision-making and action sequencing, especially in multi-agent systems.
What are Hierarchical Task Networks?
At its core, an HTN represents a task as a hierarchy of methods. Each method specifies how a task can be decomposed into a sequence of sub-tasks, or primitive actions. This decomposition allows for abstract reasoning about goals and plans, making it suitable for domains where tasks have inherent structure.
HTNs break down complex tasks into structured, hierarchical sub-tasks.
Imagine planning a trip. You don't just think 'go on vacation'. You break it down: 'book flights', 'book hotel', 'pack', 'travel'. Each of these can be further broken down. HTNs formalize this breakdown.
An HTN consists of a set of tasks, which can be either compound (decomposable) or primitive (executable actions). A task network is defined by a set of methods. Each method is associated with a compound task and specifies a sequence of sub-tasks (which can be compound or primitive) that can achieve the parent task. The planning process involves selecting appropriate methods to decompose compound tasks until only primitive actions remain, forming a valid plan.
Key Components of an HTN
| Component | Description | Role in Planning |
|---|---|---|
| Tasks | Represent actions or goals. Can be compound (decomposable) or primitive (executable). | The building blocks of the plan. |
| Methods | Decompositions of compound tasks into sub-tasks. | Provide the 'how-to' for achieving a compound task. |
| Operators/Actions | Primitive tasks that can be directly executed by an agent. | The lowest level of the plan, representing concrete actions. |
| World State | The current conditions and facts about the environment. | Used to determine which methods are applicable and to check preconditions. |
How HTNs Work: The Planning Process
HTN planning starts with an initial task (often the main goal) and the current state of the world. The planner iteratively applies methods to decompose compound tasks. This process continues until all tasks in the network are primitive actions. The order of primitive actions forms the final plan.
Loading diagram...
Advantages of HTNs
HTNs offer several advantages for agent development:
HTNs excel at handling complex, structured tasks where traditional STRIPS-style planning might struggle with the combinatorial explosion of possible action sequences.
They allow for more intuitive and human-like planning by leveraging domain knowledge encoded in methods. This makes plans more understandable and easier to debug. Furthermore, HTNs can be more efficient for certain problem classes because they prune large portions of the search space by imposing structure.
HTNs in Multi-Agent Systems
In multi-agent systems, HTNs are valuable for coordinating the actions of multiple agents. Agents can use HTNs to plan their individual contributions to a larger team goal, or to understand and react to the plans of other agents. This hierarchical decomposition helps manage the complexity of emergent behaviors and collaborative strategies.
HTNs handle complexity by breaking down tasks hierarchically, reducing the search space and allowing for more structured, domain-knowledge-driven planning.
Example: Robot Assembly Task
Consider a robot assembling a chair. The main task is 'Assemble Chair'. This can be decomposed by a method into sub-tasks like 'Attach Leg 1', 'Attach Leg 2', 'Attach Seat', 'Attach Backrest'. Each of these sub-tasks, like 'Attach Leg 1', can be further decomposed into primitive actions such as 'Pick up Leg', 'Align Leg with Seat', 'Insert Screw', 'Tighten Screw'. The HTN structure visually represents this breakdown, guiding the robot's planning process.
Text-based content
Library pages focus on text content
This hierarchical approach allows the robot to focus on one part of the assembly at a time, making the overall task manageable and efficient.
Learning Resources
Provides a foundational overview of HTN planning, its history, and core concepts.
A seminal paper introducing HTN planning, detailing its formalisms and applications.
Offers a comprehensive tutorial on HTN planning, suitable for those new to the topic.
A blog post explaining HTN planning with practical examples and its relevance in modern AI.
Lecture notes from a robotics planning course that cover HTN planning in detail.
Explores the application of HTN planning specifically for the development of intelligent agents.
A video tutorial explaining the concepts of HTN planning and its implementation in AI.
While not exclusively HTN, this lecture provides broader context on AI planning techniques.
Discusses the practical use of HTN planning in creating sophisticated AI behaviors for video games.
A collection of open-source libraries and tools for implementing HTN planners, useful for practical application.