In this portion of the text, an introduction to Numbo is given by Daniel Defays. Numbo is an attempt at modeling how a particular type of number problem is approached. The overall gist of the game is to make a goal number from a set of “bricks,” which also consist of numbers. Numbers can be used only once and only a limited set of arithmetic can be applied.
One of the first things that occurred to me about this type of problem was the human ability to approximate results. It’s mentioned in the text that one particular method of approaching the problem was to group numbers together that have a result that is close in proximity to the target number. I found myself doing this on several occasions. Naturally there are other strategies mixed in with this. The exact details are up to the player and their choice for grouping or particular operations.
It’s worth mentioning that solutions derived from raw computation via a machine are sometimes counter-intuitive when reviewed by a human. The most efficient or succinct configuration of an equation is probably not reached when done by a human, though there’s no guarantee that a machine will find one matching these criteria either. So it appears that humans have a certain preference when it comes to solving these. Even more interesting is that most humans tend to apply the same rules on similar problems. Is it that there’s a common way of thinking among humans or that certain operand/operation configurations are naturally attractive? Maybe a better question is: should we show a machine how to prefer the things we do?
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