
Explore practical ai in unity through hands-on projects, from a catch-style state machine with state pattern to behavior trees for designer-friendly ai, and genetic algorithms.
This Project and all the Projects of this course were created using Unity Version 2019.3.7f1, so if you may have trouble when you're using 2020.X. Because of that I highly advise you to install 2019.3.X in UnityHub in order to make sure the Projects work as intended.
This Project and all the Projects of this course were created using Unity Version 2019.3.x, so if you may have trouble when you're using 2020.X. Because of that I highly advise you to install 2019.3.X in UnityHub in order to make sure the Projects work as intended.
Explore the state pattern as an encapsulated, reusable approach to building state machines in Unity, with abstract state classes, enter/update/exit methods, and comparisons to simple state machines.
Explore the state pattern in Unity through a catch game, implementing an abstract state, a state machine, and concrete states like fleeing, chasing, and taunting to drive AI behavior.
Explore how behavior trees work through practical examples like door and window actions, bread crafting, and medical care, and compare them to state machines for scalable, designer-friendly AI in Unity.
I've attached the Build of the finished project to the description, so you can play around with it.
Learn to implement a behavior tree for a lumberjack in Unity using Behavior Designer, including move, chop, and deliver to storage, with variables and debugging tips.
Implement a hunter npc that hunts chickens and delivers meat to storage using a behavior tree with selectors and sequences. Compare a conditional evaluator approach and show prefab updates.
In this video I'll show you how I created the IsCarryingItem - Conditional of the Project.
Explore how behavior designer manages variables, including shared versus normal transforms, and how to share variables across tasks. Use global variables to coordinate targets across behavior trees.
Create a custom conditioner to drive hunter and lumberjack behavior: when hungry, hunter eats meat from inventory; lumberjack collects wood, retrieves meat from storage, eats, and resumes tasks.
Shows a behavior tree where a hunter eats meat when fullness is below 0.5 by checking fullness, retrieving meat from storage, and eating, using selectors to handle carrying meat.
Explore how behavior designer optimizes behavior trees by evaluating only running actions using conditional boards and aborts to switch between chopping wood and returning to base when hungry.
Implement a chicken ai in Unity with behavior designer to generate a random position around the chicken, move toward it, and drive feeding and mating via fullness and closest bush.
Explore genetic algorithms by learning selection, reproduction, and mutation as evolution-inspired forces that adapt game agents. See practical applications in tower defense AI, monster breeding, and ecology simulations.
Learn how to create state of the art AI for video games using Unity.
I believe in a practical approach in learning so this course will be primarily project-based. You won't just learn dry theory and forget everything within a matter of days but instead apply the learned concepts to two actual Unity-Projects:
Catch: A simple digital replica of play every kid's favourite game: catch
Colony Simulation: A game like Rimworld or Oxygen Not Included, where you can't control the NPCs directly, but instead they are controlled by the AI and you need to help them survive by making sure they've got enough to eat.
Also this course contains a free exclusive Behavior-Designer Educational-License, which would normally cost ~90$. Behavior Designer is the industry-standard solution for Behaviortrees and generously provided me with an exclusive Educational License, which they specifically built for this course. So you won't get it anywhere else.
Also you will of course get full lifetime access to the course.
We'll start with the simplest AI-System that there is, namely State-Machines.
After that we'll improve State-Machines and make them more extensible with the State-Pattern.
Then we'll hit the main topic which is one of the more advanced techniques that are used in Indie- and AAA-Gameproductions alike: BehaviorTrees.
Lastly we'll have a glimpse at one of the more experimental AI-Approaches, namely Genetic Algorithms, which are more of a niche topic but have the potential for very interesting game-mechanics that stand out of the crowd.
Whereas this course is already rather extensive and covers the most important aspects of AI for modern gamedevelopment, I can't possibly cover everything of this huge field. For example I won't address machine learning because this topic alone would fill a whole bunch of courses and really isn't that practical for most Game-AIs because of the lack of control you have over it.
By the end of the course you'll be confident with the most important AI-Techniques- and Design Patterns for modern Gamedevelopment.
So what are you waiting for, join me now and start creating astonishing AI-Systems yourself.