Learn Advanced AI for Games with Behaviour Trees

Learn Advanced AI for Games with Behaviour Trees, available at $84.99, has an average rating of 4.62, with 47 lectures, based on 281 reviews, and has 4978 subscribers.

You will learn about Students will learn the theory of behaviour tree design. Students will learn how to develop a behaviour tree API in C# Students will learn how to use behaviour trees to define the actions of non-player characters. This course is ideal for individuals who are Intermediate game development students wanting to extend their knowledge of artificial intelligence techniques used in games. It is particularly useful for Intermediate game development students wanting to extend their knowledge of artificial intelligence techniques used in games.

Enroll now: Learn Advanced AI for Games with Behaviour Trees

Summary

Title: Learn Advanced AI for Games with Behaviour Trees

Price: $84.99

Average Rating: 4.62

Number of Lectures: 47

Number of Published Lectures: 47

Number of Curriculum Items: 47

Number of Published Curriculum Objects: 47

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

Students will learn the theory of behaviour tree design.

Students will learn how to develop a behaviour tree API in C#

Students will learn how to use behaviour trees to define the actions of non-player characters.

Who Should Attend

Intermediate game development students wanting to extend their knowledge of artificial intelligence techniques used in games.

Target Audiences

Intermediate game development students wanting to extend their knowledge of artificial intelligence techniques used in games.

Behaviour Trees (BTs)are an A.I. architecture that provide game characters with the ability to select behaviours and carry them out, through a tree-like architecture that defines simple but powerful logic operations.  It can be used across a wide range of game genres from first-person shooters to real-time strategies and developing intelligent characters capable of making smart decisions.   The codebase is deceptively simple and yet logical, reusable and extremely powerful.  The library is written in C#and implemented in Unity 2020,however will easily port to other applications.

In this course, Penny demystifies the advanced A.I. technique of BTs used for creating believable and intelligent game characters in games, using her internationally acclaimed teaching style and knowledge from almost 30 years working with games, graphics, and having written two award-winning books on games AI. Throughout, you will follow along with hands-on workshops designed to take you through every step of putting together your own BT API.  You will build the entire BT library from the ground up, while building an art gallery simulation scenario in parallel, to test the API as you go.

Learn how to program and work with:

A Behaviour Tree Library and API that’s reusable across a wide range of game projects.

Tree architectures, nodes, leaves, sequences, and selectors that define the behaviour of individual non-player characters (NPCs).

Navigation Meshes and Agents that provide advanced path planning and navigation capabilities for characters.

A Blackboard System that acts as a global inventory for world states and allows characters to communicate with each other.

Contents and Overview

Throughout the course, you will follow along while a BT library and API are constructed from the ground up, to allow you intimate knowledge of the codebase.  Alongside this, a simple art gallery simulation will be constructed to test out the functionality of the library as it is put together.  The simulation will also rely on Unity’s NavMesh System for navigation and path planning.

The course begins with an overview of Behaviour Trees and covers all the fundamental elements (including trees, nodes, leaves, sequences, selectors, and other logical constructs). Code will be developed to navigate the Behaviour Tree and used to drive non-player characters in the art gallery including a robber, cop, visitors and workers. Throughout this, students will gain a solid knowledge of how Behaviour Trees are constructed and can be traversed, to apply actions to game characters.

At the completion of this course, students will have a fully-fledged BT library and API that they can reuse in their own game projects, to provide game characters with complex intelligent behaviours.

What students are saying about Penny’s courses:

Turns out, the hardest part of this course for me is finding the words to describe how glad I am to have enrolled in it.

I honestly love Hollistic’s teaching approach and I’ve never learned so much within a few hours about coding effectively with such detailed explanations!

Penny is an excellent instructor and she does a great job of breaking down complex concepts into smaller, easy-to-understand topics.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Overview

Lecture 2: Join the H3D Student Community

Lecture 3: Contacting H3D

Lecture 4: FAQs

Chapter 2: Behaviour Tree Concepts

Lecture 1: Introducing Behaviour Trees

Lecture 2: Nodes

Lecture 3: Tree Printing

Lecture 4: Leaf and Action Nodes

Lecture 5: NavMesh Movement

Lecture 6: Sequences

Lecture 7: Selectors

Lecture 8: Extending Action Methods

Lecture 9: Conditions

Chapter 3: Advanced Behaviours

Lecture 1: Inverters

Lecture 2: A Generic Agent Class

Lecture 3: Optimising with Coroutines

Lecture 4: Repeating Tasks

Lecture 5: Ensuring Node Status Return True States of GameObjects

Lecture 6: A Prioritising Selector

Lecture 7: Dynamically Changing Node Priorities

Lecture 8: Random Selector Challenge

Lecture 9: Shuffle and Sort Once

Chapter 4: Refactoring for Scalability

Lecture 1: Dealing with Arrays of Choice

Lecture 2: Traditional AI: Fleeing Part 1

Lecture 3: Traditional AI: Fleeing Part 2

Lecture 4: Building A Complex Behaviour Tree

Lecture 5: Cancelling Sequences with Conditions

Lecture 6: Abandoning Sequences

Lecture 7: Adding Co-dependancy Challenge

Lecture 8: Fallback Behaviours

Chapter 5: Adding New Agent Challenge

Lecture 1: Art Lovers

Lecture 2: Art Lovers Behaviour

Lecture 3: A Coroutine to Effect Agent Properties

Lecture 4: The Loop Decorator Node

Chapter 6: Environmental Factors

Lecture 1: Blackboards

Lecture 2: Integrating Blackboard State Challenge

Lecture 3: Not Daylight Robbery

Lecture 4: Agent Cooperation

Lecture 5: Interacting Agents

Lecture 6: Assigning Individual Agents to Work with Each Other

Lecture 7: Thinking like a Behaviour Tree

Lecture 8: Remember to Add Dependencies

Chapter 7: Final Challenge

Lecture 1: Cop Patrol Challenge

Lecture 2: Cop & Robber Challenge

Chapter 8: Final Words

Lecture 1: Debugging a Behaviour Tree

Lecture 2: Some Final Words from Penny

Lecture 3: Where Do I Go From Here?

Instructors

Penny de Byl
International Award Winning Professor & Best Selling Author

Penny Holistic3D
Academic, Author & Game Development Enthusiast

Rating Distribution

1 stars: 2 votes

2 stars: 7 votes

3 stars: 19 votes

4 stars: 82 votes

5 stars: 171 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!