Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This advanced course offers an in-depth exploration of behavior trees in Unity 6, focusing on the implementation of AI-driven gameplay mechanics. You'll gain the skills to build complex AI systems that respond intelligently to dynamic game environments, creating immersive player experiences. Throughout the course, you'll dive into behavior tree concepts such as sequences, selectors, and node extensions, as well as advanced techniques like dynamic priority changes and agent cooperation. The course guides you through practical applications, from setting up pathfinding in Unity to building sophisticated, scalable behavior trees. You’ll also face hands-on challenges, including a cop-and-robber scenario, to test your skills in real-world conditions. By the end, you’ll be able to implement complex AI behaviors, optimize performance, and debug AI systems effectively. This course is designed for game developers who are already familiar with Unity and want to deepen their knowledge of AI systems. It’s perfect for those looking to elevate their AI development skills to an advanced level, allowing them to create intelligent, interactive gameplay experiences. No prior AI or behavior tree knowledge is required, but a strong grasp of Unity is necessary.

Syllabus

Introduction

In this module, we will introduce the course structure, guide you through connecting with the H3D student community, and show you how to set up pathfinding in Unity. Additionally, we’ll walk you through the process of updating to Unity 6 to ensure you're working with the latest features for your game development projects.

Behaviour Tree Concepts

In this module, we will dive deep into the core concepts of behavior trees, exploring how they facilitate intelligent decision-making in AI systems. You'll learn how to create and debug behavior trees using nodes, sequences, selectors, and conditions, and also how to extend and implement custom actions. Finally, we will guide you through integrating NavMesh for smooth AI navigation.

Advanced Behaviours

In this module, we will introduce advanced techniques for enhancing behavior tree functionality, such as using inverters, repeaters, and dynamic node prioritization. You’ll learn how to optimize AI performance with coroutines and refine AI behaviors with random selectors and sorting methods. We will also explore practical implementations like creating a generic agent class and ensuring node states accurately reflect GameObject conditions.

Refactoring for Scalability

In this module, we will explore how to refactor and optimize behavior trees for scalability. You'll learn how to manage complex behaviors, including fleeing mechanics, and create dynamic trees that adapt to conditions and agent dependencies. We’ll also guide you through challenges such as adding co-dependencies and implementing fallback behaviors to ensure AI agents are always responsive and capable.

Adding New Agent Challenge

In this module, we will introduce the Art Lovers AI agents and challenge you to implement their behavior patterns. You’ll learn how to modify agent properties dynamically using coroutines and repeat behaviors efficiently with the loop decorator node, enhancing the complexity and responsiveness of your AI agents.

Environmental Factors

In this module, we will focus on the impact of environmental factors and inter-agent cooperation. You’ll learn to use blackboards for AI data management, create dynamic agent behaviors based on environmental conditions, and design collaborative behaviors that foster teamwork. We’ll also explore how to structure behavior trees to manage dependencies and optimize AI systems for more interactive and engaging gameplay.

Final Challenge

In this module, we will put your AI skills to the test with two exciting challenges. First, you’ll create dynamic AI patrols for cop characters, then implement an action-packed cop-and-robber scenario, utilizing everything you’ve learned to design responsive and intelligent AI behaviors.

Final Words

In this module, we will provide essential debugging techniques for troubleshooting and improving complex behavior trees. You’ll also hear from Penny, offering final insights and advice as you wrap up the course and continue your journey in game AI development.