Living organisms such as animals and humans have some sort of intelligence that helps us in making a particular decision to perform something. On the other hand, computers are just electronic devices that can accept data, perform logical and mathematical operations at high speeds, and output the results. So, Artificial Intelligence (AI) is essentially the subject of making computers able to think and decide like living organisms to perform specific operations.
So, apparently this is a huge subject. And there's no way that such a small book will be able to cover everything related to AI. But it is really important to understand the basics of AI being used in different domains. AI is just a general term; its implementations and applications are different for different purposes, solving different sets of problems.
Before we move on to game-specific techniques, we'll take a look at the following research areas in AI applications:
Computer vision: It is the ability to take visual input from sources such as videos and cameras, and analyze them to do particular operations such as facial recognition, object recognition, and optical-character recognition.
Natural language processing (NLP): It 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 amount of data sets available on the Web that can help researchers to do automatic analysis of a language.
Common sense reasoning: This is a technique that our brains can easily use to draw answers even from the 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 between the context, background knowledge, and language proficiency. But making machines to apply such knowledge is very complex, and still a major challenge for researchers.