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Expert systems tend not to scale very well. Once you get beyond $some_threshold rules, the decision algorithms get slow and the rules start conflicting with each other. What's needed is a meta-rule system that works during the knowledge capture phase to keep the rule set consistent.

Lots of work was done on this problem in the late 80s/early 90s but I don't know what the current SOTA is since expert systems fell out of fashion. I'd expect it to be fairly straightforward to maintain a consistent rule base with modern computers that are 1000x bigger and faster than those of the 80s.



The Rete algorithm used in OPS5 scales as O(N) where N is the number of rules - which is good.

However, it scales as O(D^2) where D is the number of working memory facts.


> I'd expect it to be fairly straightforward to maintain a consistent rule base with modern computers that are 1000x bigger and faster than those of the 80s.

Maybe not. Maybe they'll just be used to create a rule base that is 1000x bigger than those of the 80s, and you'll still have trouble maintaining consistency.




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