Tuesday, October 10, 2006

Newel and Simon restate and old definition in a different light—intelligence is finding a solution to a problem, but specifically by traversing a search space. This seems sensible; any behavior we exhibit can be encoded as a set of actions. Learning might be described as acquiring new solution sets to problems. Theorizing might be described as producing partial solution sets. Producing a search space involves establishing what in the environment is mutable and what is immutable, and how things mutable can change.

In the example of finding a solution to ax + b = cx + d a (good) solution generator would need to first identify what in the environment (this being a brow-raisingly small environment) is mutable and how. But first it must identify the rules of the environment, namely that anything done to one side must be done to the other; then the specific changes can be identified—ax, b, cx, and d can all be added, subtracted, multiplied, etc. From there, a generator can produce solutions.

So what would happen if we considered a more robust problem? Let’s consider the problem faced by a chimpanzee trapped in a room with a hole in the ceiling and some sturdy boxes in the corner. The obvious (to us and perhaps the chimp) problem is how to get out of the room? First the chimp would have to identify what in its environment is mutable—namely, the boxes and itself. Of course, it might also have to actually go over to the boxes and determine physically if the boxes are light enough to move and strong to bear the weight. But anyway, once the variables are all determined, the generation of solutions may commence. The difficult question is, then, why is it so painstakingly obvious to us that the clear solution is to place the box beneath hole?

The answer to that, according to Newell and Simon, is a heuristic. And according their main thesis, intelligent behavior would be to follow some heuristic that produced a solution with the least searching. But what heuristic is it that we follow when figuring out how to get out of the room? More importantly, how does a machine develop such a heuristic on its own?

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