“Researchers Make Computer Chess Programs More Human” on the Pure AI Web Site

I contributed to an article titled “Researchers Make Computer Chess Programs More Human” on the September 2022 edition of the Pure AI web site. See https://pureai.com/articles/2022/09/06/more-human-chess-programs.aspx.

In some machine learning scenarios, it’s useful to make a prediction system that is more human rather than more accurate. The article describes the Maia chess program which is designed to do just that.

Traditional chess programs such as Stockfish reached superhuman levels of performance about 10 years ago. In 2017, the AlphaZero chess program, based on just nine hours of deep reinforcement learning, stunned the chess and research worlds by beating Stockfish in a 100-game match by a score of 28 wins, 0 losses and 72 draws. In 2018, the Leela chess program, based on AlphaZero, was released as an open source project.

There are nine different versions of Maia: Maia 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800 and 1900. The Maia 1100 program is designed to play like a human player who is rated 1100-1199 (beginner). Maia 1500 is designed to play like a human who is rated 1500-1599, which is typical club player strength. Maia 1900 is designed to play like human who is rated 1900-1999, which is expert level but not master strength.

For a given chess position, a Maia program correctly predicts the move that would be made by a human player about 50 percent of the time. A Maia program also predicts human mistakes more accurately than standard chess programs. For moderate mistakes, Maia 1500 predicts with about 35 percent accuracy.

I was quoted in the article. “The idea of creating machine learning models that are more human rather than more accurate has interesting potential applications,” McCaffrey said. “Imagine a pilot flight training scenario. Flight simulators collect huge amounts of information. A system that predicts pilot errors could be extremely valuable.”

McCaffrey added, “Robotics is another scenario where a more human-like system could be useful. Imagine an industrial setting where humans and robots work together. A robot that is trained to be more human rather than more accurate could be safer and give an overall system that is more efficient.”

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