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In AI, a heuristic is a score generated by a mathematical function. That
score evaluates a the situation in a solution space and attempts to provide
guidance for the next step, so that the ultimate goal is reached. A
heuristic is not a generative rule. It is not a process, nor practice. It is
the result of an evaluation of a pattern.
In chess, you have a heuristic that says control the center of the board. If
your opponent moves either of his rook pawns first, then they are at a
disadvantage relative to this heuristic. There may be another heuristic at
play with that move. The problem is that there are tons of heuristics and
they are specific to one approach where another approach would come with its
own collection of heuristics. For chess masters, there is no one correct
way, but there are specific approaches that resolve another approach.
If you haven't taught your writers a collection of approaches, they will
bring their own to the table and a mess will ensue. That mess may be fine,
because each writer is optimizing the production in their view. There are
lots of metrics. Just pick up five different books on writing user manuals.
They won't agree. And, each will contain a collection of approaches. Mastery
is selecting, from all those approaches, an approach. In a departmental
context the next step would be embedding the selected approaches in a style
guide, and subsequently enforcing those approaches as editorial standards.
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