Machine-translated by Google. Questions are authored in English.
Top

Discussion

Which later technology addressed the acquisition bottleneck most effectively?

  • A.Standardised knowledge interchange formats allowing rule bases to be shared between shells
  • B.Machine learning from data
  • C.Faster inference engines that could apply a much larger number of rules within the same time
  • D.Graphical rule editors

Answer: B

Inducing the knowledge from examples sidesteps elicitation entirely, which is why learned models displaced hand-built rule bases in most diagnostic applications. Faster engines, better editors and interchange formats each helped at the margin without touching the underlying difficulty. The trade is interpretability: a learned model rarely explains itself as a rule trace does, which is why explanation is now a research topic again. Checking the units of each quantity catches a wrong formula before the arithmetic begins.

No comment is present. Be the first to comment.
Loading…

Post your comment

Please log in or register to post a comment.