which like any kind of diagnosis process is a problem of reasoning with probability. Language understanding has the same issue, like if you wrote a grammar for English you'd find that common sentences have 1000s of possible ways to parse and you will need to either make a guess or keep your options open.
MYCIN had a half-baked approach to reasoning about uncertainty that worked, one of the reasons why symbolic AI fell out of favor was that nobody developed a generally useful approach to bolt probabilities onto logic.
This was my opinion circa 2011 or so when I was recapping the old AI.
Consider, for instance, the successful early medical diagnosis program MYCIN
https://en.wikipedia.org/wiki/Mycin
which like any kind of diagnosis process is a problem of reasoning with probability. Language understanding has the same issue, like if you wrote a grammar for English you'd find that common sentences have 1000s of possible ways to parse and you will need to either make a guess or keep your options open.
MYCIN had a half-baked approach to reasoning about uncertainty that worked, one of the reasons why symbolic AI fell out of favor was that nobody developed a generally useful approach to bolt probabilities onto logic.
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Current AI is all based on probability --- and that is a problem.
https://tech.co/news/list-ai-failures-mistakes-errors
https://www.scientificamerican.com/article/why-lawyers-keep-...