Agentic-aiHow-ToBeginner · 4 min read

How to Use Planning in AI Agent: Simple Guide

To use planning in an AI agent, you define a sequence of actions the agent should take to reach a goal by simulating future steps. Planning helps the agent decide the best moves before acting, often using techniques like search algorithms or decision trees.

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Syntax

Planning in AI agents typically involves defining a state, actions, and a goal. The agent uses a planner function or method that takes the current state and goal, then returns a sequence of actions to achieve that goal.

Key parts:

state: Current situation of the agent or environment.
actions: Possible moves or steps the agent can take.
goal: Desired outcome the agent wants to reach.
planner(state, goal): Function that returns a plan (list of actions).

python

def planner(state, goal):
    # Compute sequence of actions from state to goal
    plan = []
    # Planning logic here
    return plan

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Example

This example shows a simple AI agent planning to move from a start position to a goal position on a 1D line by moving left or right.

python

def planner(state, goal):
    plan = []
    current = state
    while current != goal:
        if current < goal:
            plan.append('move_right')
            current += 1
        else:
            plan.append('move_left')
            current -= 1
    return plan

start = 2
goal = 5
plan = planner(start, goal)
print('Plan:', plan)

Output

Plan: ['move_right', 'move_right', 'move_right']

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Common Pitfalls

Common mistakes when using planning in AI agents include:

Not defining clear goals, causing the planner to fail or loop.
Ignoring possible actions or invalid moves, leading to wrong plans.
Planning without updating the state after actions, so the plan becomes outdated.
Using inefficient planning algorithms that slow down the agent.

python

def planner_wrong(state, goal):
    # Wrong: no check for goal reached, infinite loop risk
    plan = []
    current = state
    while True:
        plan.append('move_right')
        current += 1
    return plan

# Corrected version updates state and checks goal

def planner_right(state, goal):
    plan = []
    current = state
    while current != goal:
        plan.append('move_right')
        current += 1
    return plan

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Quick Reference

State: Current situation of the agent.
Actions: Possible moves the agent can take.
Goal: Desired outcome to reach.
Planner: Function that returns a list of actions from state to goal.
Update state: After each action, update the state to keep the plan valid.

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Key Takeaways

Planning lets AI agents decide a sequence of actions to reach a goal before acting.

Define clear states, actions, and goals for effective planning.

Always update the agent's state after each action to keep plans accurate.

Avoid infinite loops by checking if the goal is reached during planning.

Simple planners can use loops to simulate steps; complex ones use search algorithms.