AI-powered mechanisms as judges: Breaking ties in chess
This addresses the issue of maintaining high-quality games in chess tournaments for players and organizers, though it is incremental as it applies existing AI to a specific tiebreaking scenario.
The paper tackles the problem of tiebreakers in elite chess tournaments reducing game quality by proposing an AI-driven method that evaluates move quality against optimal engine suggestions to determine winners, demonstrating effectiveness on 25,000 grandmaster moves from 1910-2018 using Stockfish 16.
Recently, Artificial Intelligence (AI) technology use has been rising in sports to reach decisions of various complexity. At a relatively low complexity level, for example, major tennis tournaments replaced human line judges with Hawk-Eye Live technology to reduce staff during the COVID-19 pandemic. AI is now ready to move beyond such mundane tasks, however. A case in point and a perfect application ground is chess. To reduce the growing incidence of ties, many elite tournaments have resorted to fast chess tiebreakers. However, these tiebreakers significantly reduce the quality of games. To address this issue, we propose a novel AI-driven method for an objective tiebreaking mechanism. This method evaluates the quality of players' moves by comparing them to the optimal moves suggested by powerful chess engines. If there is a tie, the player with the higher quality measure wins the tiebreak. This approach not only enhances the fairness and integrity of the competition but also maintains the game's high standards. To show the effectiveness of our method, we apply it to a dataset comprising approximately 25,000 grandmaster moves from World Chess Championship matches spanning from 1910 to 2018, using Stockfish 16, a leading chess AI, for analysis.