A lot of Computer Games companies are hiring Software Designers with Artificial Intelligence experience for Games Design.
I am wondering if it is like document management that changed its name to Knowledge Management or what level AI is in the Games?
Those who play a lot of such games, please tell us your experience. Does the Game AI interact with you (for you to say wow!) or it is just more good programming billed as AI?
It is good programming billed as AI. Tackling this issue can be done in mathematics even before programming. The question is what is the underlying algorithm actually doing. It's easy to tell the difference between actual strong intelligence algorithm and an algorithm that just tries to mimic what an intelligent being would say or do.
The most advanced AI techniques deal with something in mathematics called the classification problem. The problem is to classify for example, and email as either spam or non-spam being given only the email. The classifier can have experienced other things in the past and have some memory of them however.
All current approaches have a problem called overfit. This means that the classifier works well on examples it has seen before, and examples within range of what it has seen before. But horribly on novel examples.
In practice this makes them useless. The advanced non-linear methods (multi-layer perceptrons for example) are given up in favor of computationally cheaper linear methods (PCA) which can recognize the simple cases where over-fit isn't a problem.
To give an example, suppose a classifier must determine if a picture of a room is a picture of a classroom, having been trained on various pictures. If the training sample all have green chalkboards, the classifier will identify the green pixel grouping as a useful indicator. Then if a novel picture of a classroom depicts a black chalkboard, there could be a problem. Never mind a dry erase board or even a smart board.
These approaches will never work, in order to succeed we must reverse our approach to the problem. A human being's approach might be to say something like "Does the room have something on which can be written?" <Something on which can be written> is a class, but not the one we are looking for. It's just a class that human beings generate and recognize for no other reason than that it exists. This allows them to overcome the problem of overfit.
So as you can see, the correct approach to strong AI isn't to build a classifier to solve a specific problem, but rather to build a class generator.
In other words a data mining algorithm that has some method of generalizing its results into classes. I have developed one, but the school where I attend contains mostly posers using the social network as a crutch and are afraid to look at it since they can't determine for themselves whether or not it is legit.