Here's a question I might ask an AI to solve:

"Colour the states of the USA using just 4 colours".

Now, a common heuristic a human might use is to start at one state and "work their way out". Or start at an edge state. Now this seems to work best rather than just colouring states in a random order like a computer might do. And it means a human is often better than a computer because a computer might just start colouring random states and get into trouble very quickly.

(Also I wonder if this is a learned heuristic or would a child develop this on his/her own?)

Now the question is, whether this heuristic is an innate optimisation strategy, or just laziness on the part of the human. i.e. colouring things close together takes less effort. Either way it leads to a good strategy.

But I wonder if there are any other examples of heuristics that humans inately use without realising it, that lead to good strategies?

One heuristic that computers often don't know is

"If you're trying to play a game don't keep turn around and go the other way for no reason." 

Again, a human would not do this, but again this could be laziness on the part of a human. It takes more effort to turn around than keep going in one direction to explore it.

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    $\begingroup$ Evolution has provided countless heuristics to humans for addressing problems. Following the path of least resistance. i.e. laziness, is just one of them that sometimes helps and sometimes hurts. $\endgroup$ Dec 10 '19 at 18:10
  • $\begingroup$ @George That's a good point. But it means we may have to simulate laziness in a computer even though for a computer, even for a computer it takes no physical effort to "move a pen" from one side of the screen to another. $\endgroup$
    – zooby
    Dec 10 '19 at 18:17
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    $\begingroup$ I was not trying to emphasize laziness at all but to indicate that laziness is but one of the uncountable "heuristics" humans operate under. $\endgroup$ Dec 10 '19 at 22:24

Perhaps Occam's razor counts.

Occam's razor is the meta-heuristic that "the simplest explanation is the most likely to be correct". I consider it a meta-heuristic because itself doesn't provide explanations, only means of comparing them among another. It is a heuristic because like all heuristics, it can lead to false conclusions, but is usually right.


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