Chess, one of the oldest and most universally played board games, presents an ideal testbed due to the wealth of both policy data and language data. In terms of policy data, it is reported that over ten million games are played daily on Chess.com, the most frequented online chess platform.

Technology AI Homemade chess board moves its own pieces. Maker Joshua Stanley Robotics used magnets and an open source chess platform to build this unique board. Breakthroughs, discoveries, and DIY tips sent six days a week. It's been nearly 30 years since chess champion Garry Kasparov lost to IBM's Deep Blue, marking the first time a reigning world champion was defeated by a computer in a match. Chess engines have since improved so dramatically that even a simple smartphone app can now make top grandmasters sweat .

We further transfer those PGNs into text which helps us build mixed game-language datasets from these sources.

It isn't always easy to detect cheating in chess Just three judiciously deployed cheats can turn an otherwise equal chess game into a near-certain victory, a new analysis shows - and systems designed to crack down on cheating might not notice the foul play. Daniel Keren at the University of Haifa in Israel simulated 100,000 matches using the powerful Stockfish chess engine - a computer system that, at its maximum power, is better at playing chess than any human world champion. The matches were played between two computer engines competing at the level of an average chess player - 1500 on the Elo rating scale typically used to calculate skill level in chess. Half the games were logged without any further intervention, while the other half allowed occasional intervention by a stronger computer chess "player" with an Elo score of 3190 - a higher rating than any human player has ever achieved. Competitors usually have a slim advantage when playing white, with a 51 per cent chance of winning, on average, tied to the fact that they make the game's first move.

When you purchase through links in our articles, we may earn a small commission. Picochess is a chess program for the Raspberry Pi that you can use to carry out analyses, train openings, and master games. The Picochess chess program already has a long and storied history behind it--something you should be aware of if you're looking to download and use it to play chess with on Raspberry Pi. After years of development, version 1.0 was released in 2019, but only offered minor improvements compared to 0.9N. This was followed by version 2.01 at the beginning of 2020 and 3.0 towards the end of the year.

Contemporary chess engines offer precise yet opaque evaluations, typically expressed as centipawn scores. While effective for decision-making, these outputs obscure the underlying contributions of individual pieces or patterns. In this paper, we explore adapting SHAP (SHapley Additive exPlanations) to the domain of chess analysis, aiming to attribute a chess engines evaluation to specific pieces on the board. By treating pieces as features and systematically ablating them, we compute additive, per-piece contributions that explain the engines output in a locally faithful and human-interpretable manner. This method draws inspiration from classical chess pedagogy, where players assess positions by mentally removing pieces, and grounds it in modern explainable AI techniques. Our approach opens new possibilities for visualization, human training, and engine comparison. We release accompanying code and data to foster future research in interpretable chess AI.

Your company's CEO is retiring. You search for a successor. You can promote an employee from the company familiar with the company's operations, or recruit an external professional manager. Who should you prefer? It has not been clear how to address this question, the "subject matter expertise vs. professional manager debate", quantitatively and objectively. We note that a company's success depends on long sequences of interdependent decisions, with often-opposing recommendations of diverse board members. To model this task in a controlled environment, we utilize chess - a complex, sequential game with interdependent decisions which allows for quantitative analysis of performance and expertise (since the states, actions and game outcomes are well-defined). The availability of chess engines differing in style and expertise, allows scalable experimentation. We considered a team of (computer) chess players. At each turn, team members recommend a move and a manager chooses a recommendation. We compared the performance of two manager types. For manager as "subject matter expert", we used another (computer) chess player that assesses the recommendations of the team members based on its own chess expertise. We examined the performance of such managers at different strength levels. To model a "professional manager", we used Reinforcement Learning (RL) to train a network that identifies the board positions in which different team members have relative advantage, without any pretraining in chess. We further examined this network to see if any chess knowledge is acquired implicitly. We found that subject matter expertise beyond a minimal threshold does not significantly contribute to team synergy. Moreover, performance of a RL-trained "professional" manager significantly exceeds that of even the best "expert" managers, while acquiring only limited understanding of chess.