Chapter 1: Introduction to AI

This book aims to teach you the basics of artificial intelligence (AI) programming for video games using one of the most popular commercial game engines available: Unity3D. In the upcoming chapters, you will learn how to implement many of the foundational techniques of any modern game, such as behavior trees and finite state machines.

Before that, though, you must have a little background on AI in terms of its broader, academic, traditional domain, which we will provide in this introductory chapter. Then, we'll learn how the applications and implementations of AI in games are different from other domains and the essential and unique requirements for AI in games. Finally, we'll explore the basic techniques of AI that are used in games.

In this chapter, we'll cover the following topics:

• Understanding AI
• AI in video games
• AI techniques for video games

Understanding AI

Intelligence is a natural and necessary aspect of life for all living organisms, such as animals and humans. Without intelligence - mentioned in the broadest way possible here - animals would not be able to look for food, bees would not be able to find flowers, and we humans would have never been able to craft objects or light fires, let alone develop games in Unity! On the contrary, computers are just electronic devices that can accept data, perform logical and mathematical operations at high speeds, and output the results. They lack any kind of intelligence. Computers would stay still and lifeless forever like rocks without someone telling them what to do and how to do it.

From this point of view, AI is essentially the field that studies how to give machines the spark of natural intelligence. It's a discipline that teaches computers how to think and decide like living organisms to achieve any goal without human intervention.

As you can imagine, this is a vast subject. There's no way that such a small book will be able to cover everything related to AI. Fortunately, for the goal of game AI, we do not need a comprehensive knowledge of AI. We only need to grasp the basic concepts and master the basic techniques. And this is what we will do in this book.

But before we move on to game-specific techniques, let's look at some of the main research areas for AI:

• Computer vision: This is the ability to take visual input from visual sources - such as videos and photos - and analyze them to identify objects (object recognition), faces (face recognition), text in handwritten documents (optical character recognition), or even to reconstruct 3D models from stereoscopic images.
• Natural Language Processing (NLP): This allows a machine to read and understand human languages - that is, how we write and speak. The problem is that human languages are difficult for machines to understand. Language ambiguity is the main problem: there are many ways to say the same thing, and the same sentence can have different meanings based on the context. NLP is a significant cognitive step for machines since they need to understand the languages and expressions we use before processing them and responding accordingly. Fortunately, many datasets are available on the web to help researchers train machines for this complex task.
• Machine learning: This branch of AI studies how machines can learn how to perform a task using only raw data and experience, with or without human intervention. Such tasks span from identifying if a picture contains the image of a cat, to playing board games (such as the AlphaGo software, which, in 2017, was able to beat the number one ranked player of the world in the game of Go), to perfectly interpolating the faces of famous actors in our homemade videos (so-called deepfakes). Machine learning is a vast field that spans all other AI fields. We will talk more about it in Chapter 11, Machine Learning in Unity.
• Common sense reasoning: There is a type of knowledge that is almost innate in human beings. For instance, we trivially know that things fall on the ground if they're not supported or that we cannot put a big thing into a smaller one. However, this kind of knowledge and reasoning (also called common sense knowledge) is entirely undecipherable for computers. At the time of writing, nobody knows how to teach machines such trivial - for us - things. Nevertheless, it is a very active (and frustrating) research direction.

Fortunately for us, game AI has a much narrower scope. Instead, as we will see in the next section, game AI has a single but essential goal: to make the game fun to play.

AI in video games

Different from general AI, game AI only needs to provide the illusion of intelligence. Its goal is not to offer human-like intelligent agents but characters that are smart enough to make a game fun to play.

Of course, making a game fun to play is no trivial matter, and to be fair, a good AI is just one part of the problem. Nevertheless, if a good AI is not enough to make a game fun, a bad AI can undermine even the most well-designed game. If you are interested in the problem of what makes a game fun, I suggest that you read a good book on game design, such as The Art of Game Design, by Jesse Schell.

However, for what concerns us, it is sufficient to say that it's essential to provide an adequate level of challenge to the player. A fair challenge, in this case, means the game should not be so difficult that the player can't beat the opponent, nor too easy that winning becomes a tedious task. Thus, finding the right challenge level is the key to making a game fun to play.

And that's where AI kicks in. The role of AI in games is to make it fun by providing challenging opponents and interesting Non-Player Characters (NPCs) that behave appropriately in the game world. So, the objective here is not to replicate the whole thought process of humans or animals but to make the NPCs seem intelligent by reacting to the changing situations in the game world so that they make sense to the player. This, as we mentioned previously, provides the illusion of intelligence.

Information

It is essential to mention that AI in games is not limited to modeling NPC's behaviors. AI is also used to generate game content (as we will see in Chapter 10, Procedural Content Generation) to control the story events and the narrative pace (a notable example is given by the AI director in the Left 4 Dead series) or even to invent entire narrative arcs.

Note that a good game AI doesn't need to be a complex AI. A recurring example is the AI of the original Pac-Man arcade game. By any modern standard, the algorithm that governs the behavior of the four ghosts chasing Pac-Man can barely be considered AI. Each ghost uses a really simple rule to decide where to move next: measure the distance between the ghost and a target tile and choose the direction to minimize the distance.

The target tile might be the location of Pac-Man itself (as in the case of the Red Ghost), but it can also be something in front of Pac-Man (such as the Pink Ghost) or some other tile. By simply changing the target tile's position, the Pac-Man arcade game can give each ghost a distinctive personality and an AI that challenges us even after 40 years!

The golden rule is to use the smallest amount of AI necessary to achieve the game's design goal. Of course, we may take this rule to the extreme and use no AI if we find out that it is unnecessary. For instance, in Portal and Portal 2, all the characters are completely scripted and there is no AI involved, yet nobody complained about the lack of AI.

Information

If you are interested in diving deeper into the Pac-Man AI, I suggest that you watch this very detailed video from the Retro Game Mechanics Explained YouTube channel: https://www.youtube.com/watch?v=ataGotQ7ir8.

Alternatively, if you prefer to read, you can go to this very informative web page: https://gameinternals.com/understanding-pac-man-ghost-behavior.

Another challenge for game AI is that other operations, such as graphics rendering and physics simulation, need to share the processing power that's required for AI. And don't forget that they are all happening in real time, so it's critical to achieve a steady frame rate throughout the game. This means that game AI needs to be designed to not overtake the computational resources. This is usually done by designing an algorithm that can be interrupted and spread over multiple frames.

In general AI, many companies invest in a dedicated processor for AI calculations called an AI accelerator (such as Google's Tensor Processing Unit). However, until games have widespread access to such dedicated AI processors, we game AI developers still need to pay attention to our algorithms' performance.

The next section will provide a general introduction to the most popular AI techniques that are used in video games.

AI techniques for video games

In this section, we...