Enemy AI in Unreal Engine

Introduction

When building enemy AI in Unreal Engine, developers have several tools at their disposal. The three most common approaches are Behavior Trees, State Trees, and Blueprint scripting. Each method offers a different balance between control, scalability, and ease of debugging. In this post, I’ll go over what I found when researching the use cases and modularity of these components.

Blueprint-Based AI

Blueprints tend to be the starting point for most people. The truth of it, is that you don’t need to use behavior trees or state trees for enemy AI. Blueprints can handle all the functionality that both can offer, but has its limits.

Pros:

• Great for prototyping
• Easy to visualize and debug
• No initializing or setup needed

Cons:

• Can be messy for complex behavior
• Hard to maintain or expand
• No built in prioritization or decision-making

Use case

Blueprints will be best for simple enemies like a turret to shoot at player, or enemy that walks to player when they are within range, but when your AI needs to make a decision on how to react when the player is out of range or the enemy’s health is low and should make smarter decisions. Behavior trees may be the better option.

Behavior Trees

Behavior trees have been the standard for AI over the few years. They use a priority task based system read from left to right. The behavior tree nodes have decorators or service that act as observers or boolean states to help guide the AI in making decisions.

How it works

Behavior Trees have three main nodes. Sequence, Selector, and Parallel. They all can have a decorator or service that the AI uses to make decisions.

Sequence: As the name suits, it follows a sequence from left-to-right. Each task under this node will be followed left to right and then back to the left until it is told to stop.

Selector: This helps with decisions as well. Selector can hold different sequences that have their own decorators to dictate which task to run. For example, a decorator of lowHealth on the left side to prioritize this task, and if it isn’t true run task on the right which could be attackPlayer.

Parallel: Allows the AI to run multiple tasks at the same time: For example decorator to keep Line of Sight of the player, sequence to move to player and attack. It can be configurable heavily, but its mostly used to run background checks for current tasks.

Pros

• Clear visual structure for complex logic
• Easy to reuse tasks across multiple AIs.
• Supports dynamic decision-making and prioritization

Cons

• Can become complex to debug when trees get large
• Without concise decorators/services can run into many edge case scenarios

Use Case

Ideal for AIs that need layered, reactive decision-making

State Trees

State Trees are Unreal’s newer approach to high-performance, data-driven AI logic. They combine the best of Behavior Trees and traditional finite state machines (FSMs), offering a more direct way to manage states and transitions.

A Finite State Machine is one of the oldest and most widely used models for game AI.

It’s based on the idea that an AI character can only be in one “state” at a time, and that transitions between states are triggered by conditions or events.

It works well for a large world with multiple AI agents. It has a system for Mass AI which is exclusive to state trees. Works for cluster of enemies and highly optimized. Mass AI don’t rely on a AI controller that blueprints or behavior trees would use. State trees essentially become the brain for the AI.

Pros:

• Cleaner representation of state-based logic
• Lightweight and performant for large-scale crowds

Cons

• Not yet as widely adopted or tested for all scenarios
• Newer system, less documentation and examples

Use Case

Best for modular, scalable AI especially in large worlds or when using Mass AI.

My Takeaways

I would think for someone with little experience outside of blueprints I would recommend blueprinting then transitioning to either state trees or behavior tree. I think there could be a strong case for merging state trees and behavior trees. It felt like as I kept looking into both, you could use State trees to dictate the states and behavior trees to give a much better decision making for the tasks.

Think of the State Tree as the AI’s director, deciding the scene; and the Behavior Tree as the actor, deciding how to perform it.

It is taking the better state method the State Tree offers while granting the robust decision making from the behavior trees.

Conclusion

While looking into the different methods for enemy behavior. I realized each approach has its strengths. Blueprints are quick and intuitive for simple enemies. Behavior Trees are great for handling complex decisions, and State Trees aer best for managing lot of agents efficiently. I also think that there is a robust connection between using both behavior trees and state trees together to create a highly reactive AI. I will continue to look into State Trees to integrate them more in my projects going forward. Understanding how these systems work together gabe me a clearer picture of how to create more maintainable and smarter AIs for different scenarios.