Planning in Artificial Intelligence

So far, we have seen how AI agents search for solutions and make decisions. However, many real-world problems require more than choosing a single move. They require planning a sequence of actions to reach a goal.

Planning in Artificial Intelligence focuses on deciding what actions to take, in what order, and under what conditions to achieve a desired outcome.

What Is Planning in AI?

Planning is the process of generating a step-by-step strategy that moves an agent from an initial state to a goal state.

Each step in a plan represents an action that changes the current state of the environment.

In simple terms, planning answers the question:

“What should I do now, and what should I do next, to reach my goal?”

Real-World Examples of Planning

Planning is everywhere in daily life and technology.

• Google Maps planning a route with multiple turns
• A robot planning steps to assemble a product
• An AI assistant planning tasks for your day
• A delivery system planning package drop-offs

In all these cases, the system must consider the order of actions, constraints, and outcomes.

Key Components of an AI Planning Problem

Every planning problem consists of four main elements:

• Initial State: Where the agent starts
• Actions: What the agent can do
• Transition Model: How actions change the state
• Goal State: What the agent wants to achieve

A valid plan is a sequence of actions that transforms the initial state into the goal state.

Classical Planning Example

Consider a simple task:

Goal: Make tea

• Initial state: Kettle is empty, water is cold
• Actions: Fill kettle, boil water, pour water
• Goal state: Tea is ready

The plan is the correct order of these actions.

Representing Planning in Code

Below is a simple Python example that represents planning as a sequence of actions.

def plan_to_make_tea():
    steps = [
        "Fill the kettle with water",
        "Boil the water",
        "Pour hot water into the cup"
    ]
    return steps

plan = plan_to_make_tea()
for step in plan:
    print(step)
  

Code Explanation

This example demonstrates a very basic planning idea.

• Each string represents an action
• The order of actions matters
• The plan achieves the goal step by step

Real AI planners dynamically generate such plans instead of hardcoding them.

Output Explanation

The output shows the exact sequence of actions required to complete the task successfully.

If the order were incorrect, the goal would not be achieved.

Types of Planning in AI

AI planning can be categorized into several types:

• State-space planning: Search through states
• Goal-based planning: Focus on achieving goals
• Conditional planning: Handle uncertainty
• Hierarchical planning: Break tasks into sub-tasks

Different applications use different planning strategies depending on complexity.

Why Planning Is Important

Without planning, AI systems would react randomly instead of acting purposefully.

• Planning enables long-term decision making
• It reduces wasted actions
• It improves efficiency and reliability

Planning is a core component of robotics, automation, logistics, and intelligent agents.

Practice Questions

Quick Quiz

Coming up next: Knowledge Representation — how AI stores facts, rules, and relationships.