I’m playing my strategy game and I have to spend 10 minutes finding their units
while they automatically know where mine are, which type they are, their
energies, and so on. It’s not the fact that they cheat to make the game harder, it’s
the fact that they cheat because the artificial intelligence is very weak. The
computer adversary should know just about the same information as the player.
If you look at a unit, you don’t see their health, their weapons, and their bullets.
You just see a unit and, depending on your units, you respond to it. That’s what
the computer should do; that’s what artificial intelligence is all about.
In this chapter I will first give you a quick overview of several types of artificial
intelligence, and then you will see how you can apply one or two to games. In this
chapter, I’m going to go against the norm for this book and explain the concepts
with little snippets of code instead of complete programs. The reason I’m doing
this is because the implementation of each field of artificial intelligence is very
specific, and where is the fun in watching a graph give you the percentage of the
decisions if you can’t actually see the bad guy hiding and cornering you?
Complete examples would basically require a complete game! For this reason, I
will go over several concrete artificial intelligence examples, giving only the
theory and some basic code for the implementation, and it will be up to you to
choose the best implementation for what you want to do.
Here is a breakdown of the major topics in this chapter:
Understanding the various fields of artificial intelligence
Using deterministic algorithms
Recognizing finite state machines
Identifying fuzzy logic
Understanding a simple method for memory
Using artificial intelligence in games
The Fields of Artificial Intelligence
There are many fields of artificial intelligence; some are more game-oriented and
others are more academic. Although it is possible to use almost any of them in
games, there are a few that stand out, and they will be introduced and explained
in this section.
Expert Systems
Expert systems solve problems that are usually solved by specialized humans. For
example, if you go to a doctor, he will analyze you (either by asking you a set of
questions or doing some analysis himself), and according to his knowledge, he
will give you a diagnosis.
An expert system could be the doctor if it had a broad enough knowledge base. It
would ask you a set of questions, and depending on your answers, it would
consult its knowledge base and give you a diagnosis. The system checks each of
your answers with the possible answers in its knowledge base, and depending on
your answer, it asks you other questions until it can easily give you a diagnosis.
For a sample knowledge tree, take a look at Figure 20.1. As you can see, a few
questions would be asked, and according to the answers, the system would follow
the appropriate tree branch until it reached a leaf.
Genetic Algorithms
Using genetic algorithms is a method of computing solutions that relies on the
concepts of real genetic concepts (such as evolution and hereditary logic). You
might have had a biology class in high school that explained heredity, but in case
you didn’t, the field of biology studies the evolution of subjects when they
reproduce. (Okay, maybe there is a little more to it than that, but we are only
interested in this much.)
As you know, everyone has a blood type, with the possible types being A, B, AB,
and O, and each of these types can be either positive or negative. When two
people have a child, their types of blood will influence the type of blood the child
has. All that you are is written in your DNA. Although the DNA is nothing more
than a collection of bridges between four elements, it holds all the information
about you, such as blood type, eye color, skin type, and so on. The little ‘‘creatures’’
that hold this information are called genes.
What you might not know is that although you have only one type of blood, you
have two genes specifying which blood type you have. How can that be? If you
have two genes describing two types of blood, how can you have only one type of
blood?
Neural Networks
Neural networks attempt to solve problems by imitating the workings of a
brain. Researchers started trying to mimic animal learning by using a collection
of idealized neurons and applying stimuli to them to change their behavior.
Neural networks have evolved much in the past few years, mostly due to the
discovery of various new learning algorithms, which made it possible to
implement the idea of neural networks with success. Unfortunately, there still
aren’t major discoveries in this field that make it possible to simulate the human
brain efficiently.
The human brain is made of around 50 billion neurons (give or take a few
billion). Each neuron can compute or process information and send this
information to other neurons. Trying to simulate 50 billion neurons in a computer
would be disastrous. Each neuron takes various calculations to be simulated,
which would lead to around 200 billion calculations. You can forget about
modeling the brain fully, but you can use a limited set of neurons (the human
brain only uses around 10 percent of its capacity for conscious thought) to mimic
basic actions of humans.
In 1962, Rosenblatt created something called a perceptron, one of the earliest
neural network models. A perceptron is an attempt to simulate a neuron by using
a series of inputs, weighted by some factor, which will output a value of 1 if
the sum of all the weighted inputs is greater than a threshold, or 0 if it isn’t.
Figure 20.6 shows the idea of a perceptron and its resemblance to a neuron.
While a perceptron is just a simple way to model a neuron, many other ideas
evolved from this, such as using the same values for various inputs, adding a bias
or memory term, and mixing various perceptrons using the output of one as
input for others. All of this together formed the current neural networks used in
research today.
There are several ways to apply neural networks to games, but probably the most
predominant is by using neural networks to simulate memory and learning. This
field of artificial intelligence is probably one of its most interesting parts, but
unfortunately, the topic is too vast to give a proper explanation of it here.
Fortunately, neural networks are becoming more and more popular these days,
and numerous publications are available about the subject.
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