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Comment by fryguy | Hacker News Reader

I see this as an extrapolation of an expert system, which is an AI. It seems the more you know about how they work, the less they seem like artificial intelligence and more like some simple optimization problem. I suppose this is what the Chinese Room (http://en.wikipedia.org/wiki/Chinese_room) is all about.

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I don't think it truly qualifies as an expert system because, to the best I can determine from this limited article, it doesn't make any inferences from a base set of rules. Rather, it is provided with a comprehensive set of all rules, and attempts to find the best solution that satisfy those rules (or, in the language of operations research, constraints). The value provided then is that, because it can consider the space of solutions efficiently, it may discover solutions which are not obvious to human operators.

The way these systems are made, and as the article mentions, is that the obvious rules are added, and solutions evaluated. Experience humans will then usually discover something about the solution that is impossible/impractical, but was not included in the provided rules. The new rules/constraints, and the process repeats until humans are comfortable with the feasibility of the solution/schedule.

That's a good point. This system is more of a constraint solver than an expert system. It's not really answering questions than optimizing one (big and dynamic) scheduling problem.

I feel the same way about machine learning algorithms and even deep learning. The more you learn about how the algorithms work, the more it seems like just simple averaging of numbers. I think this is a good thing though, because it removes the mysticism behind it.

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