Creating the illusion of life

Before ping in much deeper, we should stop for a moment and define intelligence. Intelligence is simply the ability to learn something then apply that knowledge. Artificial intelligence, at least for our purposes, is the illusion of intelligence. Our intelligent entities need not necessarily learn things, but must at the very least convince the player that they are learning things. I must stress that these definitions fit game AI specifically. As we'll discover later in this section, there are many applications for AI outside of games, where other definitions are more adequate. 

Intelligent creatures, such as humans and other animals, learn from their environment. Whether it's through observing something visually, hearing it, feeling it, and so on, our brains convert those stimuli into information that we process and learn from. Similarly, our computer-created AI must observe and react to its environment to appear smart. While we use our eyes, ears, and other means to perceive, our game's AI entities have a different set of sensors at their disposal. Rather than using big, complex brains like ours, our code will simulate the processing of that data and the behaviors that model a logical and believable reaction to that data.

AI and its many related studies are dense and varied, but it is important to understand the basics of AI being used in different domains before digging deeper into the subject. AI is just a general term; its various implementations and applications are different for different needs and for solving different sets of problems.

Before we move onto game-specific techniques, let's take a look at the following research areas in AI applications that have advanced tremendously over the last several decades. Things that used to be considered science fiction are quickly becoming science fact, such as autonomous robots and self-driving cars. You need not look very far to find great examples of advances in AI—your smartphone most likely has a digital assistant feature that relies on some new AI-related technology. It probably knows your schedule better than you do! Here are some of the research fields driving AI:

• Computer vision: This is the ability to take visual input from sources, such as video and photo cameras, and analyze it to perform particular operations such as facial recognition, object recognition, and optical-character recognition. Computer vision is at the forefront of advances in autonomous vehicles. Cars with even relatively simple systems, such as collision mitigation and adaptive cruise control, use an array of sensors to determine depth contextually to help prevent collisions.
• Natural language processing (NLP): This is the ability that allows a machine to read and understand the languages as we normally write and speak. The problem is that the languages we use today are difficult for machines to understand. There are many different ways to say the same thing, and the same sentence can have different meanings according to the context. NLP is an important step for machines since they need to understand the languages and expressions we use before they can process them and respond accordingly. Fortunately, there's an enormous number of datasets available on the web that can help researchers by doing automatic analysis of a language.
• Common sense reasoning: This is a technique that our brains can easily use to draw answers even from domains we don't fully understand. Common sense knowledge is a usual and common way for us to attempt certain questions since our brains can mix and interplay context, background knowledge, and language proficiency. But making machines apply such knowledge is very complex and still a major challenge for researchers.
• Machine learning: This may sound like something straight out of a science fiction movie, and the reality is not too far off. Computer programs generally consist of a static set of instructions, which take input and provide output. Machine learning focuses on the science of writing algorithms and programs that can learn from the data processed by said program, and apply that for future learning.