![]() ![]() In fact, I’ll use the simplest possible such puzzle, the 3-puzzle. To illustrate what such a formula would look like, I’ll turn to a class of puzzles that’s often used in introductory AI classes to illustrate search algorithms like A*, namely the sliding-block puzzle family that contains the 8-puzzle and the 15-puzzle. (These formulas would be very complicated, but they do exist nevertheless.) For example, for both games and puzzles we could have formulas that take the (encoded) game or puzzle state as input and result in a value that corresponds to the best possible gameplay move or change to the puzzle state. To me, it would have been way more interesting had they actually generated a program that can play the game optimally (without searching).Īnyway, reading about this result made me think of formulas that can play games such as checkers and chess (or really any type of game) or that can solve puzzles optimally, and how such formulas could be generated. From the outside looking in, that just isn’t terribly exciting. So, yeah, it’s a draw assuming perfect play, and it took several computers brute-forcing through large portions of the game tree for 18 years to show it’s a draw. I haven’t read the Science article, but as far as I understand, this isn’t “solving” checkers in the strong sense, only in a weak sense. (The result is actually a few months old, but American news media were busy reporting about freeway chases and other important events and didn’t notice until the result was published in Science.) Additional information is available from the University of Alberta website. Assuming perfect play, the game is a draw. It was reported today that Jonathan Schaeffer and his Games Group team at University of Alberta have solved checkers.
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