Overview
Imagine you're planning a cross-country road trip with multiple stops, budget constraints, and time limitations. You don't just jump in the car and drive randomly—you break down the big goal (reach your destination) into smaller, manageable tasks: plan the route, book accommodations, budget for gas, identify interesting stops along the way. You consider different path options, compare their trade-offs, and create a step-by-step plan.
This is exactly what AI agents do through planning algorithms. While ReAct patterns allow agents to think and act iteratively, many complex tasks require systematic decomposition and strategic planning before execution. Planning algorithms give agents the ability to look ahead, consider multiple options, and construct optimal sequences of actions to achieve their goals.
Learning Objectives
After completing this lesson, you will be able to:
- Understand different types of planning problems and when to use each approach
- Implement search-based planning algorithms (BFS, DFS, A*)
- Build hierarchical task networks for complex planning scenarios
- Choose appropriate planning algorithms based on problem characteristics
- Integrate planning algorithms with agent architectures
The Nature of Planning Problems
From Actions to Plans
Single Action: "Calculate 15% of $1,250" Simple Sequence: "Find a restaurant, make a reservation, get directions" Complex Plan: "Plan a week-long vacation within budget, considering weather, activities, and logistics"
Planning becomes essential when:
- Tasks have multiple steps with dependencies
- Actions have preconditions and effects
- Resources are limited and must be allocated efficiently
- Multiple paths exist to achieve the goal
- Mistakes are costly and should be avoided
Planning Complexity Visualization
Algorithm Characteristics:
| Algorithm | Optimal | Complete | Time Complexity | Space Complexity | Best For |
|---|---|---|---|---|---|
| BFS | Yes | Yes | O(b^d) | O(b^d) | Short plans, small spaces |
| DFS | No | No* | O(b^m) | O(bm) | Memory-limited, deep spaces |
| A* | Yes* | Yes* | Varies | O(b^d) | Good heuristics available |
| HTN | No | No | Varies | Varies | Complex, hierarchical tasks |
With appropriate conditions (admissible heuristic for A, finite depth for DFS)
Connections to Previous Concepts
Building on Agent Architectures and Tool Integration
Planning algorithms enhance the agent patterns we've learned:
From Agent Architectures:
- ReAct vs Planning: ReAct is reactive planning, while these algorithms do lookahead planning
- Hybrid Agents: Planning provides the deliberative layer in hybrid architectures
- Plan-and-Execute: These algorithms power the "Plan" phase
From Tool Integration:
- Action Representation: Planning actions map directly to tool calls
- Preconditions: Tool requirements become action preconditions
- Effects: Tool outputs become action effects
- Workflows: Planning generates tool execution sequences