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Originally shared by Rob Jongschaap
... The rules of games like chess and go are prescriptive, somewhat complicated and never change. They are, in the context of AI, “well bounded.” A book teaching chess or go written 100 years ago is still relevant today. Training an AI to play one of these games takes advantage of this “boundedness” in a variety of interesting ways, including letting the AI decide how it will play.
Now, however, imagine the rules of chess could change randomly at any time in any location: Chess on Tuesdays in Chicago has one set of rules but in Moscow there are a different set of rules on Thursdays. Chess players in Mexico use a completely different board, one for each month of the year. In Sweden the role for each piece can be decided by a player even after the game starts. In a situation like this it’s obviously impossible to write down a single set rules that everyone can follow at all times in all locations. This is an example of an unbounded problem. ...
... The rules of games like chess and go are prescriptive, somewhat complicated and never change. They are, in the context of AI, “well bounded.” A book teaching chess or go written 100 years ago is still relevant today. Training an AI to play one of these games takes advantage of this “boundedness” in a variety of interesting ways, including letting the AI decide how it will play.
Now, however, imagine the rules of chess could change randomly at any time in any location: Chess on Tuesdays in Chicago has one set of rules but in Moscow there are a different set of rules on Thursdays. Chess players in Mexico use a completely different board, one for each month of the year. In Sweden the role for each piece can be decided by a player even after the game starts. In a situation like this it’s obviously impossible to write down a single set rules that everyone can follow at all times in all locations. This is an example of an unbounded problem. ...
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