# Building Multi-Agent Systems Using NetLogo: A Beginner's Guide to Agent-Based Modeling

> Learn to build multi-agent systems with NetLogo using our beginner's guide. Explore theoretical concepts, code, and assignments for visualizing emergent intelligence. Start your agent-based modeling journey today.

- Repository: [Microsoft/AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners)
- Tags: getting-started
- Published: 2026-08-02

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**The microsoft/AI-For-Beginners repository teaches multi-agent systems through NetLogo, providing theoretical guides, code snippets, and hands-on assignments in `lessons/6-Other/23-MultiagentSystems/` to help learners visualize emergent intelligence from simple interacting agents.**

Building multi-agent systems using NetLogo offers an accessible entry point into artificial intelligence through visual, agent-based modeling. The microsoft/AI-For-Beginners curriculum includes a dedicated module that bridges theoretical AI concepts with practical simulation, requiring only the NetLogo runtime from Northwestern University's official distribution. This hands-on lesson demonstrates how parallel execution of agent code creates complex emergent behaviors like flocking and viral spread.

## Curriculum Structure and Location

The NetLogo content lives within the broader 12-week AI program outlined in [`AGENTS.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/AGENTS.md). Rather than embedding a full runtime inside the repository, the lesson at [`lessons/6-Other/23-MultiagentSystems/README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/6-Other/23-MultiagentSystems/README.md) provides theoretical foundations and executable code snippets that learners run in the standalone NetLogo environment.

## Core Concepts in Agent-Based Modeling

### Agent Types and Architecture

NetLogo operates with three primary agent types that execute code in parallel:

- **Turtles**: Mobile agents that move around the world and interact with each other
- **Patches**: Stationary grid cells that form the environment and hold local state
- **Observer**: The global controller that initializes simulations and manages execution flow

### Essential NetLogo Commands

The lesson emphasizes core primitives that define multi-agent interactions:

- `create-turtles` - Spawns new agents into the world
- `ask` - Broadcasts instructions to specific agent sets
- `breed` - Defines custom agent categories with specialized behaviors
- `to go [...] end` - Procedure definitions that control simulation steps

## The Learning Flow

The structured lesson follows a four-phase pedagogical approach:

1. **Pre-lecture quiz** - Validates existing knowledge before diving into emergent intelligence concepts
2. **Conceptual reading** - Explains agent taxonomy, collective behavior theory, and the role of NetLogo in visualizing decentralized systems
3. **Hands-on exploration** - Students load built-in models like *Flocking* or *Wolves-Sheep* to inspect Interface, Code, and World objects
4. **Practical assignment** - The [`assignment.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/assignment.md) file directs learners to modify the virus model to simulate real-world scenarios such as COVID-19 spread

## Code Examples from the Source

The [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md) provides executable snippets demonstrating minimal NetLogo syntax for building multi-agent simulations.

**Creating and moving turtles:**

```netlogo
create-turtles 10
ask turtles [
  forward 10
]

```

**Defining custom breeds with unique behaviors:**

```netlogo
breed [cats cat]
create-cats 5
ask cats [
  set color orange
  forward random 5
]

```

**Implementing flocking alignment rules:**

```netlogo
to align
  ask turtles [
    let avg-heading mean [heading] of other turtles in-radius 5
    set heading avg-heading
  ]
end

```

## Practical Application: The Assignment

The [`lessons/6-Other/23-MultiagentSystems/assignment.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/6-Other/23-MultiagentSystems/assignment.md) file challenges students to customize existing models from the NetLogo Model Library. Typical modifications include adjusting infection rates in the virus model or implementing social distancing behaviors to visualize pandemic dynamics, directly connecting abstract AI concepts to current events.

## Summary

- The microsoft/AI-For-Beginners repository teaches **building multi-agent systems using NetLogo** through a structured lesson in `lessons/6-Other/23-MultiagentSystems/`
- Learners download the external NetLogo runtime and explore the **Model Library** rather than running code embedded in notebooks
- Core concepts include **turtles**, **patches**, and **observer** agents executing commands like `ask` and `breed` in parallel
- The curriculum demonstrates **emergent behaviors**—such as flocking alignment and viral spread—arising from simple local rules
- Practical assessment involves modifying the **virus model** to simulate real-world COVID-19 scenarios

## Frequently Asked Questions

### Do I need programming experience to start building multi-agent systems in NetLogo?

No prior coding is required to begin. The microsoft/AI-For-Beginners NetLogo module starts with conceptual foundations and provides copy-paste code snippets. NetLogo's visual interface and Logo-based syntax make it accessible to beginners while still supporting complex simulations through the `breed` and `ask` primitives.

### Where can I find the NetLogo lesson files in the repository?

All materials reside in `lessons/6-Other/23-MultiagentSystems/`, including the main [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md) with theoretical content and code examples, plus [`assignment.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/assignment.md) with the hands-on customization task. Translated versions exist in the `translations/` subdirectories for non-English learners.

### How does the assignment work for the NetLogo module?

The assignment requires downloading NetLogo from the official Northwestern University site and opening a built-in model (typically the virus simulation). Students then modify parameters like infection probability or agent movement patterns to model real-world scenarios such as COVID-19 spread, demonstrating how simple rule changes affect emergent collective behavior.

### What is the difference between turtles and patches in NetLogo?

**Turtles** are mobile agents that execute behaviors like `forward` and `set heading`, while **patches** are stationary grid cells that represent the environment. The observer coordinates the simulation flow, and together these three agent types enable parallel execution that generates complex emergent phenomena like flocking or traffic patterns.