Humans have entertained themselves with board games for thousands of years. Some, like chess and go, continue to be played today. However, many ancient games have been lost to history, with only boards and pieces found in places like the tomb of King Tutankhamun giving us hints about the games. The rules of the Royal Game of Ur were discovered on a Babylonian cuneiform tablet housed in the British Museum.
Without ancient rulebooks, researchers have had to make educated guesses about how to play games like the oldest complete game found in a Bronze Age cemetery in Shahr-i Sokhta, Iran, so they have turned to AI.
Now AI and computer simulations are further enhancing the reconstruction of these games. One of the goals is that, in future, if a game like the one found in Shahr-i Shokhta is unearthed, an AI would be able to suggest methods for how it was actually played. "One key method is AI-driven rule generation, where algorithms simulate various plausible rulesets based on the game's structure," says Piette.
Another way that AI can help is testing how the myriad permutations of possible rules play out, to find out which are fun and which lead to tedium. This is done by breaking the game down into units of game-playing information and feeding these "ludemes" into an AI.
One of the first case studies for this AI approach has been Ludus Latrunculorum – one of the ancient games that we know the most about because of historical writings. "This gave our reconstruction process the best chance of success," says Cameron Browne at Maastricht University in the Netherlands.
Simulations were run with boards of various sizes found for the Roman game Ludus Latrunculorum. Researchers found that the game "became mind-numbingly long as the board size increased," which should serve as a warning for modern board game designers and people who insist on using house rules for Monopoly.
Previously: Tabletop gamer's gift guide for 2024
