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- Herausgeber: Packt Publishing
- Sprache: Englisch
- Veröffentlichungsjahr: 2019
Learn to build intelligent and responsive Non-Player Characters for your games with Unreal Engine Game AI.
Key Features
- Understand the built-in AI systems in Unreal Engine for building intelligent games
- Leverage the power of Unreal Engine 4 programming to create game AI that focuses on motion, animation, and tactics
- Learn to profile, visualize, and debug your Game AI for checking logic and optimizing performance
Book Description
Learning how to apply artificial intelligence ( AI ) is crucial and can take the fun factor to the next level, whether you're developing a traditional, educational, or any other kind of game. If you want to use AI to extend the life of your games and make them challenging and more interesting, this book is for you.
The book starts by breaking down AI into simple concepts to get a fundamental understanding of it. Using a variety of examples, you will work through actual implementations designed to highlight key concepts and features related to game AI in UE4. You will learn to work through the built-in AI framework in order to build believable characters for every game genre (including RPG, Strategic, Platform, FPS, Simulation, Arcade, and Educational). You will learn to configure the Navigation, Environmental Querying, and Perception systems for your AI agents and couple these with Behavior Trees, all accompanied with practical examples. You will also explore how the engine handles dynamic crowds. In the concluding chapters, you will learn how to profile, visualize, and debug your AI systems to correct the AI logic and increase performance.
By the end of the book, your AI knowledge of the built-in AI system in Unreal will be deep and comprehensive, allowing you to build powerful AI agents within your projects.
What you will learn
- Get an in-depth knowledge about all the AI Systems within Unreal Engine
- Create complex AIs, understanding the art of designing and developing Behavior Tree
- Learn how to perform Environmental Queries (EQS)
- Master the Navigation, Perception, and Crowd Systems
- Profile and Visualize the AI Systems with powerful debugging tools
- Extend every AI and Debug system with custom nodes and functions
Who this book is for
Hands-On Artificial Intelligence with Unreal Engine is for you if you are a game developer with a bit experience in Unreal Engine, and now want to understand and implement believable game AI within Unreal Engine. The book will be both in Blueprint and C++, allowing people from every background to enjoy the book. Whether you're looking to build your first game or expand your knowledge to the edge as a Game AI Programmer, you will find plenty of exciting information and examples of game AI in terms of concepts and implementation, including how to extend some of these systems.
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Hands-On Artificial Intelligence with Unreal Engine
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First published: April 2019
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ISBN 978-1-78883-565-7
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Contributors
About the author
Francesco Sapio received his Master of Science in Engineering in Artificial Intelligence and Robotics degree from Sapienza University, Rome, Italy, a couple of semesters in advance, graduating with summa cum laude; he is currently a researcher at the same university. He is an Unreal and Unity 3D expert, skilled game designer, and an experienced user of major graphics programs. He helped the development of many games (both educational and commercial). He is also an active writer on the topic of game development. Finally, Francesco loves math, philosophy, logic, and puzzle solving, but most of all, creating video games—thanks to his passion for game designing and programming.
About the reviewers
Lauren S. Ferro has a Ph.D. in player profiling and modeling. She is currently an Adjunct Professor and researcher at Sapienza University, in Rome. Lauren is also the co-organizer of the Unreal Engine Meetup in Rome. In addition, she created the game design resource Gamicards, which is a prototyping tool for game experiences. At heart, she is intrigued by how we interact with the world and those in it.
Katax Emperor is originally from Melbourne, Australia. He received bachelor's degree in Computer Hardware Engineering and two master's degree in Multimedia at Swinburne University of Technology in 2012.
Katax is involved in particle system programming, developing web-based interactive applications such as games, making animation with 3D applications for use in Unreal Engine, and also doing VJ performance with real-time improvisation style. Katax started programming with Commodore 64 and started making digital art works with Commodore Amiga 500. After Windows 98 was released, he switched to PC. He expanded his experience with 3D applications, fractal and graphic design, and video editing programs. Today, he has a wide range of experience in a number of applications for artists, designers, and developers.
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Table of Contents
Title Page
Copyright and Credits
Hands-On Artificial Intelligence with Unreal Engine
Dedication
About Packt
Why subscribe?
Packt.com
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Section 1: The Unreal Framework
Making the First Steps in the World of AI
Before starting...
Prerequisites
Installing and preparing the software
Unreal Engine
Visual Studio
Becoming an AI Game Developer
What it means to be an AI Game Developer
AI in the game development process
A bit of history
What is AI?
A glance into the past
AI in games
AI in games – Industry and Academia
Planning our journey
Technical terminology
The bottom-up approach
The agent schema
Unreal Engine AI Framework
A sketch of our journey
Decision-making with Behavior Trees (chapters 2, 6, 8, 9, and 10)
Navigation (chapters 3 and 7)
Environment Query System (chapters 4 and 12)
Agent Awareness (chapters 5 and 12)
Crowds (chapter 7)
Designing Behavior Trees (chapters 8, 9, and 10)
Debugging methods for Game AI (chapters 11, 12, and 13)
Going beyond (chapter 14)
Enabling AI for C++ users
Summary
Behavior Trees and Blackboards
How Behavior Trees work
The structure of a Mathematical Tree
Behavior Tree components
Root
Tasks
Composite
Decorators
Service
Blackboards and their integration with Behavior Trees
Creating our AI project
Starting Behavior Trees from AI Controllers
Creating the Behavior Tree and the Blackboard 
Creating the Blackboard
Creating the Behavior Tree
The AI Controller to run a Behavior Tree
AI Controller in Blueprint
AI Controller in C++
Summary
Navigation
What to expect from a Navigation System
Unreal Navigation System
Generating the Navigation Mesh
Setting parameters for the Nav Mesh
Display settings
Generation settings
Project Settings
Settings on the Character Movement Component
Modifying the Navigation Mesh
Nav Modifier Volume
Nav Mesh Areas
Creating a NavArea class in Blueprint
Creating a NavArea class in C++
Nav Link Proxy
Creating a Nav Link Proxy
Simple Links and Smart Links
Both Simple and Smart Links
Simple Links
Smart Links
Other settings of the Nav Link Proxy
Extending the Nav Link Proxy
Navigation Avoidance
Navigation Filtering
Creating a Navigation Filter in Blueprint
Creating a Navigation Filter in C++
Overriding the Navigation System
Summary
Environment Querying System
Enabling the Environment Querying System
Understanding the Environment Querying System
The general mechanism of EQS
The components of an Environment Query
Environment Queries
Contexts
Generators
Tests
Visual representation of the components
Running an Environmental Query within a Behavior Tree
Not only Locations, but also Actors!
Exploring the built-in nodes
Built-in Contexts
Built-in Generators
Actors Of Class
Current Location
Composite
Points: Circle
Points: Cone
Points: Donut
Points: Grid
Points: Pathing Grid
Built-in Tests
Visualizing Environment Queries
Creating components for the Environment Querying System
Creating Contexts
Creating the Player Context in Blueprint
Creating the Player Context in C++
Creating Generators
Creating Tests
Summary
Agent Awareness
Perception of AI in video games
Sound
Footsteps
Knocking over objects
Position
Zone of proximity
Interacting with other enemies
It isn't all about the "enemy"
Perceptive AI isn't just humanoid or animalistic
Impact of Players
Overview of the Sensing System
The AIPerceptionComponent
AIPerceptionComponent in Blueprint
Sense – Sight
Sense – Hearing
AIPerceptionComponent and Senses in C++
Different Teams
The AIStimuliSourceComponent
All Pawns are automatically detected
AIStimuliSourceComponent in Blueprint
AIStimuliSourceComponent in C++
Hands-on with the perception system – Sight AI Controller
A Blueprint perception system
A C++ perception system
Testing it all
Summary
Extending Behavior Trees
A quick recap on Behavior Trees
Creating a Task
Creating a Blueprint Task
Creating a Task in C++
Creating a Decorator
Creating a Decorator in Blueprint
Creating a Decorator in C++
Creating a Service
Creating a Service in Blueprint
Creating a Service in C++
Creating a Composite Node
Creating new type of nodes or new types of Trees
Summary
Crowds
Crowds in games
Building believable crowds
Animals
Crowd movement
Crowd dynamics and creating realistic behavior
Flow-based
Entity-based
Agent-based
Crowds in Unreal
Reciprocal Velocity Obstacles (RVO)
RVO in Unreal
Advanced RVO settings
RVO in Blueprint
RVO in C++
RVO observations
RVO resources
Detour Crowd
How the Detour Crowd system works
Using the Detour Crowd system
Detour Crowd settings
Debugging the Detour Crowd Manager
More crowd resources
Summary
Section 2: Designing and Implementing Behavior Trees
Designing Behavior Trees - Part I
The Expected Behavior
Building the Nodes
Nodes that already exist
Decorator – check variable
Check Variable Blueprint implementation
Check Variable C++ implementation
Task – Find Random Location
Find Random Location Blueprint implementation
Find Random Location C++ implementation
AI Controller
Implementing the AI Controller in Blueprint
Implementing the AI Controller in C++
Using the AI Controller
Summary
Designing Behavior Trees - Part II
Setting up the environment for testing the Behavior Tree
Creating the Player
Creating the Chasing Agent
Preparing the Level
Service – Update Chasing Behavior
Update Chasing Behavior Blueprint implementation
Update Chasing Behavior C++ implementation
Summary
Designing Behavior Trees - Part III
Building the Behavior Tree
Setting up the Blackboard
Building the Tree
Running the Behavior Tree
Running the Behavior Tree using the Blueprint controller
Running the Behavior Tree using the C++ controller
Bug Correction
Improving further (C++ only)
Node Name
A better description for the Decorator
Filtering Blackboard Keys
Summary
Section 3: Debugging Methods
Debugging Methods for AI - Logging
Basic Logging
Console Logging and on-screen messages in Blueprint
On-screen messages in C++
Console Logging in C++
Creating a Custom Logging Category (C++)
Summary
Debugging Methods for AI - Navigation, EQS, and Profiling
Debugging behavior trees
Profiling and visualizing environmental queries
Visualizing environmental queries with the EQS testing pawn
Creating the EQS testing pawn
Creating a test environmental query
Settings of the EQS testing pawn to visualize environmental queries
Profiling environmental queries
Setting up the test assets for EQS profiling
The environmental query profiler
Saving and loading EQS stats
Testing and visualizing the navigation mesh
Visualizing the navigation mesh
Navigation test actor
Profiling AI
Profiling with the console
Creating a custom stat group
Session frontend
Summary
Debugging Methods for AI - The Gameplay Debugger
The anatomy of the Gameplay Debugger
Gameplay Debugger Extensions
Gameplay Debugger categories
Category 0 – Navmesh
Category 1 – AI
Category 2 – Behavior Tree
Category 3 – EQS
Category 4 – Perception
The Nav Grid Category
Multiple Categories on-screen
More Categories
Gameplay Debugger settings
Extending the Gameplay Debugger
Creating a module with the use of a new plugin
Setting up the module to work with the Gameplay Debugger
Creating a new Gameplay Debugger Category
Registering the Gameplay Debugger Category
Visualizing the custom Gameplay Debugger Category
Creating an Extension for the Gameplay Debugger
The structure of the Extension
Creating the Extension class
Registering the Extension
Summary
Going Beyond
Why is psychology an important consideration for AI?
Psychology and AI in games
Building better AI through psychology
Machine learning
Uses of AI and machine learning in games
Uses for AI in games
Uses for machine learning in games
Adaptive behavior
What we haven't covered in this book
Motion and navigation (low level)
Racing games
Agent awareness
Environment Query System
Decision-making and planning
Discrete games/board games
Learning techniques
Procedural Content Generation (PCG)
Search and optimization
Multi-Agent Systems
AI and psychology
Going beyond classical AI
Much, much, much more!
Some useful resources
AI and philosophy
Goodbye
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
Learning how to apply AI is crucial and can take the fun factor to the next level when developing traditional, educational, or any other kind of game. Unreal Engine is a powerful game development engine that allows you to create 2D and 3D games. If you want to use AI to extend the life of your games and make them challenging and more interesting, this book is for you.
The book starts with breaking down artificial intelligence (AI) into simple concepts to get a fundamental understanding of it. Using a variety of examples, you will work through actual implementations designed to highlight key concepts and features related to game AI in UE4. You will learn how to work through the built-in AI framework to build believable characters for every genre (e.g. RPG, Strategic, Platform, FPS, Simulation, Arcade, and Educational games). You will learn how to configure Navigation, Environmental Querying and Sensory systems for your AI agents and couple it with Behavior Trees, all with practical examples. You will then explore how the engine handles dynamic crowd. In the concluding chapters, you will learn how to profile, visualize and debug your AI systems, to correct the AI logic, and increase performance.
By the end of the book, your AI knowledge of the built-in AI system in Unreal will be deep and comprehensive, allowing you to build powerful AI agents within your projects.
Who this book is for
This book is for you if you are a game developer with a bit experience in Unreal Engine, and now want to understand and implement believable game AI within Unreal Engine. The book will be both in Blueprint and C++, allowing people from every background to enjoy the book. Whether you're looking to build your first game or to expand your knowledge as a Game AI Programmer, you will find plenty of exciting information and examples of game AI in terms of concepts and implementation, including how to extend some of these systems.
What this book covers
Chapter 1, Moving the First Steps in The AI World, explores the prerequisites to becoming an AI game developer and how AI is used in the game development pipeline.
Chapter 2, Behavior Trees and Blackboards, introduces you to two principal structures used within the Unreal AI framework, which are used to control most AI agents in games. You will learn how to create a Behavior Tree and how these can store data within Blackboards.
Chapter 3, Navigation, teaches you how an agent can navigate or find a path through a map or the environment.
Chapter 4, Environment Query System, helps you get to grips with making an Environmental Query, which is a subsystem of the Unreal AI Framework for spatial reasoning. Mastering them is key in implementing believable behaviors within Unreal.
Chapter 5, Agent Awareness, deals with how an AI agent can sense the world and the surrounding environment. These include sight, sound, and potentially any sense you might imagine (by extending the system).
Chapter 6, Extending Behavior Trees, takes you through the task of extending behavior trees with Unreal by using Blueprint or C++. You will learn how to program new Tasks, Decorators, and Services.
Chapter 7, Crowds, explains how to handle crowds within the Unreal AI Framework that offer some functionality.
Chapter 8, Designing Behavior Trees – PartI, focuses on how to implement a Behavior Tree so that the AI agent can chase our player in the game (both in Blueprint and C++). This chapter, along with the next two, explores this example from designing to implementation.
Chapter 9, Designing Behavior Trees – Part II, is a continuation of the previous chapter. In particular, we will build the last missing piece of the puzzle (a custom Service) before we build our final Behavior Tree in the next chapter.
Chapter 10, Designing Behavior Trees – Part III, is a continuation of the previous chapter and is the final part of Designing Behavior Trees. We will finish what we started. In particular, we will build the final Behavior Tree and make it run.
Chapter 11, Debugging Methods for AI – Logging, examines a series of methods that we can use to debug our AI systems, including console logging, on-screen messages in Blueprint, and many more. By mastering the art of logging, you will be able to easily keep track of your values and which part of the code you are executing.
Chapter 12, Debugging Methods for AI – Navigation, EQS, and Profiling, exploresa number of more specific tools for the AI systems that are incorporated within Unreal Engine. We will see some more tools for analyzing performance related to AI code, as well as tools to visualize Environmental Queries and the Navigation Mesh.
Chapter 13, Debugging Methods for AI – the Gameplay Debugger, gets you to explore the most powerful debugging tool and the best friend of any Unreal AI developer—Gameplay Debugger. This chapter will take you even a step further, by teaching how to extend this tool so to customize it to your needs.
Chapter 14, Going Beyond, concludes with some suggestions on how to explore the concepts presented (and others) beyond this book and some thoughts regarding AI.
To get the most out of this book
Being comfortable with using Unreal Engine 4 is an important starting point. The objective of this book is to take those who work with the technology to a level where they are comfortable enough with all aspects to be a leader and driver of that technology on a project.
Download the example code files
You can download the code bundle for this book from here: https://github.com/PacktPublishing/Hands-On-Artificial-Intelligence-with-Unreal-Engine.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/9781788835657_ColorImages.pdf.
Get in touch
Feedback from our readers is always welcome.
General feedback: If you have questions about any aspect of this book, mention the book title in the subject of your message and email us at [email protected].
Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you would report this to us. Please visit www.packt.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details.
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Reviews
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For more information about Packt, please visit packt.com.
Section 1: The Unreal Framework
In this section, we are going to explore in depth the built-in Unreal AI Framework. We will start from what are Behavior Trees and Blackboard, and we will proceed in learning about the Navigation System, the Environmental Querying System and the Perception System. Towards the end of this section, we will also understand how Unreal handles large Crowds, and how we can extend (both in Blueprint and C++) our Behavior Trees, by creating custom Tasks, Decorators and Services.
The following chapters will be covered in this section:
Chapter 1
, Moving the First Steps in the AI World
Chapter 2
, Behavior Trees and Blackboards
Chapter 3
, Navigation
Chapter 4
, Environment Querying System
Chapter 5
, Agent Awareness
Chapter 6
,
Extending Behavior Trees
Chapter 7
,
Crowds
Making the First Steps in the World of AI
From the bronze giant Talos, to Symbolic Systems and Neural Networks: how AI has been shaped and used in video games.
Welcome, reader, to the beginning of our journey in Artificial Intelligence, or AI for short. Have you ever wondered about how those hard-working peons in Warcraft explore complicated maps? Or, how those perky ghosts in Pac-Man can get you wherever you are? Or perhaps how your opponents in Final Fantasy optimize their attacks to slaughter your team?
Then you've come to the right place!
In this chapter, we will explore the prerequisites to becoming an AI game developer and how AI is used in the game development pipeline. Then, we will take a look at the history of AI in general and in video games, and learn how the conjunctive effort of many brilliant minds built AI as we know it today. Afterwards, we will discuss the AI Framework underneath the Unreal Engine, since this book will focus on Unreal.
Finally, we will plan our journey and get a general overview of the topics that the different chapters in this book deal with.
Before starting...
... I'd like to reply to some questions that some of you might already have.
Does this book take into account Blueprint or C++?
This book will explain both, so don't worry.
If you don't know C++, you can follow along with the Blueprint part of this book, and if you so wish, you can try the C++ one.
If, on the other hand, you are a programmer that prefers C++, then don't worry! This book will explain how to deal with AI in Unreal using C++.
There are so many books around about AI, why should I choose this one?
Different books explain different aspects of AI, and often they are not mutually exclusive, but rather they complement each other.
However, the major point of interest of this book is that it is a good compromise between the theory of the different AI systems that are present in Unreal and practical applications, since the whole book is full of concrete examples.
Does this book provide a test project/materials to work on?
Absolutely, yes. You will be able to download the content for this book from the following link: http://hog.red/AIBook2019ProjectFiles (the link is case-sensitive).
I am already using the Unreal Engine for Artificial Intelligence, is this book good for me?
This all depends on your level of knowledge. In fact, in the first part of this book, we will talk mainly about the AI framework that's built into the Unreal Engine and how to use it. This is probably the part you are more familiar with if you have had some experience with Unreal for AI. However, this book will dive deep in these topics, that even an expert could find some useful tips. The second part, instead, will discuss some debugging methods for Game AI and will also explain how to extend them (mainly with C++). Feel free to have a look at the outline, and decide if this book suits you.
I am already using another game engine, is this book still good for me?
Well, as much as I'd like to say that this is a general book about AI, it isn't—at least not completely. Although the main focus will still be toward the main concepts of AI, we will look at implementing them in Unreal. However, this book will heavily rely on the built-in AI framework in Unreal. Thus, I encourage you to read more generalized books about AI to get a better understanding. On the other hand, you can always try. Maybe, by understanding some concepts here, other books will be easier to read and you will be able to transfer this knowledge into the game engine of your choice.
I'm a student/teacher, is this book suitable for teaching in class?
Absolutely, yes. I know how important it is to find good resources when teaching a class, and I'm writing this book with this intention in mind. As a result, regardless of whether you are a student or a teacher, you will find a section at the end of each chapter with some exercises that you can improve your skills this (or propose them to your students, if you are a teacher). Also, you can find some more integrative material here: http://hog.red/AIBook2019LearningMaterial (the link is case-sensitive).
Is this book going to cover everything, everything, and everything about AI in Unreal and all its systems?
Well, despite my best efforts to describe each of the systems in great detail, covering everything is an impossible task, due also to the complexity of such big engine. However, I'm confident to say that this book covers most of the aspects related to each AI system within the Unreal Engine, including how to extend the built-in systems and how to efficiently perform debugging. Thus, I can certainly say that this book is very comprehensive.
Prerequisites
Since this book is targeted at people who are getting started with AI in game development, I will not assume any prior/background knowledge in AI. However, please take the following into account:
Blueprint users
: You should be familiar with Blueprint programming, and know how a Blueprint graph works in general.
C++ users
: You should be familiar with programming, especially with the C family languages (such as C, C#, C++, or even Java), and in particular with C++, since it's the language that Unreal Engine uses. Being familiar
with the Unreal Engine C++ API is a great bonus, although not strictly required. So, even if you are not a master, don't worry—follow along and you will learn.
Also, it would be great if you had some knowledge about vector math and physical kinematic principles – at least the ones commonly used in video games. Either way, in case you are rusty on those, don't worry too much, since it is not required for this book; however, it is nice to have if you're looking for a job as an AI Developer.
Installing and preparing the software
Before you continue reading, let's install the software we need. In particular, we will need Unreal Engine and Visual Studio.
Unreal Engine
Let's talk about Unreal Engine. After all, this is a book about how to develop game AI within this wonderful game engine.
Unreal Engine is a game engine that was developed by Epic Games. It was first released in 1998, and nowadays it is one of the most used (open) game engines (alongside Unity) due to its powerful features. The following screenshot shows the main interface of Unreal Engine:
We need to have the latest version of Unreal Engine installed. You can find it by visiting https://www.unrealengine.com/en-US/what-is-unreal-engine-4 . Unless you took Unreal Engine from the source code (https://docs.unrealengine.com/en-us/Programming/Development/BuildingUnrealEngine), you will have the Epic Launcher installed. If you are a Blueprint user, and do not intend to use C++, then this is enough for you. If, on the other hand, you will be using C++, you will need to perform a couple of extra steps.
When installing the Engine, you will need to check some options (if you are using C++). In particular, we need to check that we have both "Engine Source" and "Editor symbols for debugging", as shown in the following screenshot:
By doing so, we will be able to navigate through the C++ Engine Source and also have a full stack of calls in the case of a crash (so you will know what went wrong).
Visual Studio
You won't need this if you are using Blueprint—this is for C++ users only.
In fact, we will need an IDE to edit our C++ code. We are going to use Visual Studio, since it's well-integrated with Unreal. You will be able to download Visual Studio Community Edition for free through the official website, https://www.visualstudio.com, or from https://visualstudio.microsoft.com/vs/.
You may also find this short guide on how to set up Visual Studio so that it works with Unreal Engine useful: https://docs.unrealengine.com/en-us/Programming/Development/VisualStudioSetup.
Once you have everything installed and ready to go, we can continue with the rest of this chapter.
Becoming an AI Game Developer
Ever dreamed of becoming an AI Game Developer? Or maybe just be able to write "intelligent" programs? Then this book is for you!
However, I need to advise you that this is no easy task.
Game development and design are some of the most extensive works of art around. This is due to a large amount of expertise that is required to bring a game to life. You can get an idea of this by just looking at the final credits in a game. They are endless and contain names of people who have dedicated a lot of their time to the game in various roles. AI development is a central part of this big process, and it requires years to master it, like most things in life. As such, iteration is key, and a great place to get started in this book.
What it means to be an AI Game Developer
First of all, you will need to master maths, physics, and coding. Also, you will mostly probably work in an interdisciplinary team, which includes artists, designers, and programmers. In fact, you will probably work with existing proprietary software technology, and it's required that you are able to build new technology to fulfill the project's technical requirements. You will be asked to research coding techniques and algorithms so that you keep yourself up to date on technological developments and advancements in the game industry and to identify technical and developmental risks/obstacles and generate solutions to overcome identified risks.
On the other hand, you will be able to give life to characters and entities in video games. After all the frustrations that you might go through, you will be the first one to assist, or better, generate an intelligent behavior in games. It takes time and is quite challenging, so don't be too hard on yourself in the early stages. Once you achieve some real AI in your game that can think on its own, this is an achievement worthy of rewarding yourself for.
For beginners in AI, this book will help you set the first stones toward that goal. For experts, this book will provide a useful guide to refresh the different AI systems in Unreal, and explore in-depth features that might help you in your work.
AI in the game development process
The game development pipeline might vary a lot, depending on which studio you visit, but all of them lead to the creation of a video game. This is not a book about pipelines, so we won't explore them, but it's important to have a rough idea of where AI is collocated.
In fact, AI intersects with many parts of the game development pipeline. Here are some of the main ones:
Animation
: It might surprise some of you, but there is a lot of research going on regarding this topic. Sometimes, animation and AI overlap. For instance, one of the problems that developers need to solve consists of how we can procedurally generate hundreds of animations for characters, which can behave realistically, and also how they interact with each other. In fact, solving the inverse kinematic (IK) is a mathematical problem, but which of the infinite solutions to choose to achieve a goal (or just to provide a realistic look) is an AI task. We won't face this specific problem during this book, but the last chapter will provide pointers to places where you can learn more about this.
Level Design
: If a game automatically produces levels, then AI plays an important role in that game.
Procedural Content Generation
(PCG) in games is a hot topic at the moment. There are games that are entirely based on PCG. Different tools to procedurally generate height maps can help Level Designers achieve realistic looking landscapes and environments. This is indeed a wide topic to explore.
Game Engine
: Of course, inside the game engine, there are many AI algorithms that come into play. Some of these are specific for agents, while others just improve the engine's features and/or tasks. These represent the most vast category, in which they can vary from simple algorithms to adjust a Bezier curve based on the context, to implementing behavior trees or finite state machines for animations. Under the hood, there is a lot going on here. We will explore some of these concepts in this book, but the message to take home is that an algorithm can be adapted to solve similar problems in different fields. In fact, if Finite State Machines (FSMs) are used to make decisions, why not use them to "
decide
" which animation to play? Or why not even handle the whole game logic (i.e. the blueprint visual scripting in Unreal Engine)?
Non-Player Characters
(
NPCs
): This is the most visible example of using AI in games, and this is also the most obvious AI to the Player (we will explore more about the relationship between the AI and the player in
Chapter 14
, G
oing Beyond
). This is what most of this book is focused on; that is, from moving the character (for instance, with a Pathfinding Algorithm) to making decisions (i.e. with Behavior trees), or collaborate with other NPCs (multi-agent systems).
Unfortunately, we don't have the space to deal with all of these topics in this book. Therefore, we will just be focusing on the last part (NPCs), and explore the AI Framework that's built into Unreal.
A bit of history
Before venturing on our journey, I believe that having a general overview of the history of AI and AI in games might be beneficial. Of course, you can skip this part if you are a more hands-on type of person who cannot wait to getting down to programming AI.
What is AI?
This is a very interesting question, which doesn't have a unique answer. In fact, different answers lead to different aspects of AI. Let's explore some (of many) definitions that have been given by different scholars (in chronological order).
Actually, Russell and Norvig, in their book, organized these specific definitions into four categories. Here is their schema:
We don't have the time to go into detail about the question "What is AI?", since this could fill up another book on its own, but the last chapter of this book will also include some philosophical reference where you can expand your knowledge on this topic.
A glance into the past
It might come unexpected to some of you, but the story of AI started well before computers. In fact, even ancient Greeks hypothesized the existence of intelligence machines. A famous example is the bronze giant Talos, who protected the city of Crete from invaders. Another is the golden helpers of Hephaestus, who helped God in his volcano forge along with the Cyclops. In the XVII century, René Descartes wrote about automatons that could think, and believed that animals were do different from machines, which could be replicated with pulleys, pistons, and cams.
However, the core of this story starts in 1931, when the Austrian logician, mathematician, and philosopher Kurt Gödel proved that all the true statements in the first-order logic are derivable. On the other hand, this is not true for higher order logics, in which some true (or false) statements are unprovable. This made first-order logic a good candidate to automate derived logical consequences. Sounds complicated? Well, you can imagine how that sounded to the ears of his traditionalist contemporaries.
In 1937, Alan Turing, an English computer scientist, mathematician, logician, cryptanalyst, philosopher, and theoretical biologist, pointed out some of the limits of "intelligent machines" with the halting problem: it is not possible to predict a-priori if a program will terminate unless it is actually run. This has many consequences in theoretical computer science. However, the fundamental step happened thirteen years later, in 1950, when Alan Turing wrote his famous paper "Computing Machinery and Intelligence", in which he talked about the imitation game, nowadays mostly known as "The Turing Test": a way to define what an intelligent machine is.
In the 1940s, some attempts were made to emulate biological systems: McCulloch and Pitts developed a mathematical model for a neuron in 1943, and Marvin Minsky created a machine that was able to emulate 40 neurons with 3,000 vacuum tubes in 1951. However, they fell into the dark.
From the late 1950s through to the early 1980s, a great portion of AI research was devoted to "Symbolic systems". These are based on two components: a knowledge base made out of symbols and a reasoning algorithm, which uses logical inference to manipulate those symbols, in order to expand the knowledge base itself.
During this period, many brilliant minds made significant progresses. A name worth quoting is McCarthy, who organized a conference in Dartmouth College in 1956, where the term "Artificial Intelligence" was first coined. Two years later, he invented the high-level programming language LISP, in which the first programs that were able to modify themselves were written. Other remarkable results include Gelernter's Geometry Theorem Prover in 1959, the General Problem Solver (GPS) by Newell and Simon in 1961, and the famous chat-bot Eliza by Weizenbaum, which was the first software that, in 1966, could have a conversation in natural language. Finally, the apotheosis of symbolic systems happened in 1972 with the invention of PROLOG by the French scientist Alain Colmerauer.
Symbolic systems led to many AI techniques, which are still used in games, such as blackboard architectures, pathfinding, decision trees, state machines, and steering algorithms, and we will explore all of them throughout this book.
The trade-off of these systems is between knowledge and search. The more knowledge you have, the less you need to search, and the faster you can search, the less knowledge you will need. This has even been proven mathematically by Wolpert and Macready in 1997. We will have the chance to examine this trade-off in more detail later in this book.
At the beginning of the 1990s, symbolic systems became inadequate, because they proved hard to scale to larger problems. Also, some philosophical arguments arose against them, maintaining that symbolic systems are an incompatible model for organic intelligence. As a result, old and new technologies have been developed that were inspired by biology. The old Neural Networks were dusted off from the shelf, with the success of Nettalk in 1986, a program that was able to learn how to read aloud, and with the publication of the book "Parallel distributed processing" by Rumelhart and McClelland in the same year. In fact, "back-propagation" algorithms were rediscovered, since they allow a Neural Network (NN) to actually learn.
In the last 30 years of AI, research took new directions. From the work of Pearl on "Probabilistic reasoning in intelligent systems", probability has been adopted as one of the principal tools to handle uncertainty. As a result, AI started to use many statistical techniques, such as Bayesian-nets, Support Vector Machines (SVMs), Gaussian processes, and the Markov Hidden Model, which is used widely to represent the temporal evolution of the states of a system. Also, the introduction of large databases unlocked many possibilities in AI, and a new whole branch named "Deep Learning" arose.
However, it's important to keep in mind that, even if AI researchers discover new and more advance techniques, the old are not to be discarded. In fact, we will see how, depending on the problem and its size, a specific algorithm can shine.
AI in games
The history of AI in video games is as interesting as what we talked about in the previous section. We don't have the time to go through it in detail and analyze every single game and how each one of them contributed to the field. For the most curious of you, toward the end of this book, you will find other lectures, videos, and books where you can dive deeper into the history of AI in games.
The first form of AI in video games was rudimental and used in games like Pong [Atari, 1972], Space Invaders [Midway Games West, Inc., 1978], and so on. In fact, beyond moving a paddle to try and catch a ball, or moving aliens toward the player, there wasn't much more we could do:
The first renowned game that used a noticeable AI was Pac-Man [Midway Games West, Inc., 1979]. The four monsters (later named ghosts due to a flickering port in the Atari 2600) used an FSM to chase (or flee from) the player:
During the 1980s, AI in games didn't change much. It was only with the introduction of Warcraft: Orcs & Humans [Blizzard Entertainment, 1994] that a pathfinding system was successfully implemented in a video game. We will explore the Navigation system in Unreal in Chapter 3, Navigation:
The game that probably started to sensitize people about AI was Goldeneye 007 [Rare Ltd., 1997], which showed how AI could improve gameplay. Although it was still relying on FSMs, the innovation was that characters could see each other, and act accordingly. We will explore agent-awareness in Chapter 5, Agent Awareness. This was a hot topic at the time, and some games made it the main game mechanic, such as Thief: The Dark Project [Looking Glass Studios, Inc., 1998]:
Another hot topic was modeling a soldiers' emotions during battles. One of the first games that implemented an emotional model was Warhammer: Dark Omen [Mindscape, 1998], but it was only with Shogun: Total War [The Creative Assembly, 2000] that these models were used with extreme success with a large number of soldiers without performance issues:
Some games even made AI the core of the gameplay. Although one of the first games to do so was Creatures [Cyberlife Technology Ltd., 1997], the concept is more evident in games like The Sims [Maxis Software, Inc., 2000] or Black and White [Lionhead Studios Ltd., 2001]:
In the last 20 years, many AI techniques have been adopted and/or developed. However, if the game doesn't require an advanced AI, you will probably find FSMs, still being broadly used, along with Behavior trees, which we will start exploring soon in Chapter 2, Behavior Trees and Blackboards.
AI in games – Industry and Academia
When it comes to comparing AI that's applied to video games, both in Academia and in the Industry, there is a big discrepancy. I would say that there was almost a fight between the two. Let's have a look at the reasons behind this. In fact, they have very different goals.
Academia wants to create AI agents for games that think intelligently to act in the environment and interact with the player.
On the other hand, the game industry wants to create AI agents for games that seem to think intelligently to act in the environment and interact with the player.
We can clearly notice that the first leads to a more realistic AI, whereas the second leads to a more believable AI. Of course, commercial games are more worried about the latter rather than the former.
We will explore this concept in more detail in Chapter 14, Going Beyond, when we talk about the psychology and game design involved in creating an AI system for games. In fact, to achieve a believable behavior, you often need to try and be realistic as well.
However, in more formal terms, we can say that game AI falls in the category of weak AI (opposite to strong AI), which focuses on solving a specific task or problem intelligently, rather than develop a consciousness behind it. In any case, we won't venture further into this.
Planning our journey
It's finally time to start planning our journey, before jumping into the next chapter.
Technical terminology
Since, for some of you, this is your first journey into AI, it's important that you have a small glossary of the technical terms that are used throughout this book (and in general, in AI). We have already encountered some of these in the past few pages:
Agents
are systems that are capable of autonomous reasoning toward solving a specific set of goals.
Backward Chaining
is the process of tracing the cause of a problem by working backwards.
Blackboard
is an architecture for exchanging data between different agents, and sometimes even within the agent itself (especially in Unreal).
Environment
is the world where an agent lives. For instance, the game world is the environment of an NPC from the same game. Another example is a chess board, which represents the environment of a system that plays chess against humans (or other systems).
Forward Chaining
, opposite to Backward Chaining, is the process to work forward to find the solution to a problem.
Heuristic
is a practical approach to problem-solving, which does not guarantee to be optimal, nor sufficient for immediate goals. Heuristic methods are used when finding the optimal solution to a problem is impractical (if not impossible), in order to find a satisfactory solution. They can be thought of as mental shortcuts to lighten cognitive load during a decision-making process. Sometimes, it can represent the knowledge of an agent based on his/her past experience (although this is often given a-priori). The term "
Heuristic
" derives from ancient Greek, with the meaning of "
find
" or "
discover
".
