By Ian Millington
Read or Download Artificial Intelligence for Games Companion CD-ROM (The Morgan Kaufmann Series in Interactive 3D Technology) PDF
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Additional resources for Artificial Intelligence for Games Companion CD-ROM (The Morgan Kaufmann Series in Interactive 3D Technology)
First, from an engineering point of view, the early successes on simple problems didn’t seem to scale to more difficult problems. It might be easy to develop AI that understands (or appears to understand) simple sentences, but understanding a full human language seemed no nearer. Second, from a philosophical viewpoint, symbolic approaches weren’t biologically plausible. You can’t understand how a human being plans a route by using a symbolic route planning algorithm any more than you understand how human muscles work by studying a forklift truck.
Every natural computing field has had to make optimizations to the basic model to get sensible results. And these optimizations are, by and large, distinctly un-biological. The no-free-lunch theorem and subsequent work has shown that, over all problems, no single approach is better than any other. The only way any algorithm can outperform another is to focus on a specific set of problems. The narrower the problem domain you focus on, the easier it will be for the algorithm to shine. Which, in a roundabout way, brings us back to the golden rule of AI: search (trying possible solutions) is the other side of the coin to knowledge (knowledge about the problem is equivalent to narrowing the number of problems your approach is applicable to).
For example, a group of characters come across an ambush. One of the ambushers is wearing phased force-field armor. Only the new, and rare, laser rifle can penetrate it. One character has this rifle. When they select who to attack, the most constrained heuristic comes into play: it is rare to be able to attack this enemy, so that is the action that should be taken.