Understanding Artificial Intelligence

Artificial intelligence ( AI ) is defined as the techniques used to emulate the human thought process in a computer. This is a pretty general definition for AI, as it should be; AI is a very broad research area ”with game- related AI being a relatively small subset of the whole of AI knowledge. The goal in this hour is not to explore every facet of AI because that would easily fill an entire book, but rather to explore the fundamental concepts behind AI as they apply to games .

As you might have already guessed, human thought is no simple process to emulate, which explains why AI is such a broad area of research. Even though there are many different approaches to AI, all of them basically boil down to attempting to make human decisions within the confines of a computer "brain." Most traditional AI systems use a variety of information-based algorithms to make decisions, just as people use a variety of previous experiences and mental rules to make a decision. In the past, the information-based AI algorithms were completely deterministic , which means that every decision could be traced back to a predictable flow of logic. Figure 20.1 shows an example of a purely logical human thought process. Obviously, human thinking doesn't work this way at all; if we were all this predictable, it would be quite a boring planet!

Figure 20.1. A completely logical human thought process involves nothing more than reason.

Eventually, AI researchers realized that the deterministic approach to AI wasn't sufficient to accurately model human thought. Their focus shifted from deterministic AI models to more realistic AI models that attempted to factor in the subtle complexities of human thought, such as best-guess decisions. In people, these types of decisions can result from a combination of past experience, personal bias, or the current state of emotion ”in addition to the completely logical decision making process. Figure 20.2 shows an example of this type of thought process. The point is that people don't always make scientifically predictable decisions based on analyzing their surroundings and arriving at a logical conclusion. The world would probably be a better place if we did act like this, but again, it would be awfully boring!

Figure 20.2. A more realistic human thought process adds emotion and a dash of irrationality with reason.

The logic flow in Figure 20.1 is an ideal scenario in which each decision is made based on a totally objective logical evaluation of the situation. Figure 20.2 shows a more realistic scenario, which factors in the emotional state of the person, as well as a financial angle (the question of whether the person has insurance). Examining the second scenario from a completely logical angle, it makes no sense for the person to throw the hammer because that only slows down the task at hand. However, this is a completely plausible and fairly common human response to pain and frustration. For an AI carpentry system to effectively model this situation, there would definitely have to be some hammer throwing code in there somewhere!

This hypothetical thought example is meant to give you a tiny clue as to how many seemingly unrelated things go into forming a human thought. Likewise, it only makes sense that it should take an extremely complex AI system to effectively model human thought. Most of the time, this statement is true. However, the word "effectively" allows for a certain degree of interpretation, based on the context of the application requiring AI. For your purposes, effective AI simply means AI that makes computer game objects more realistic and engaging.

More recent AI research has been focused at tackling problems similar to the ones illustrated by the hypothetical carpentry example. One particularly interesting area is fuzzy logic , which attempts to make "best-guess" decisions rather than the concrete decisions of traditional AI systems. Another interesting AI research area in relation to games is genetic algorithms , which try to model evolved thought similarly to how scientists believe nature evolves through genetics . A game using genetic algorithms would theoretically have computer opponents that learn as the game progresses, providing the human player with a seemingly never ending series of challenges.