We thank Jiacheng Liu for his work on collecting chess-related data and chess book list. B.1 Universal Chess Interface (UCI) The UCI format is widely used for communication between chess engines and user interfaces. It represents chess moves by combining the starting and ending squares of a piece, such as "e2e4" to indicate moving the pawn from e2 to e4. SAN (Standard Algebraic Notation) is a widely used notation system in the game of chess for recording and describing moves. It provides a standardized and concise representation of moves that is easily understood by chess players and enthusiasts. In SAN, each move is represented by two components: the piece abbreviation and the destination square. The piece abbreviation is a letter that represents the type of piece making the move, such as "K" for king, "Q" for queen, "R" for rook, "B" for bishop, "N" for knight, and no abbreviation for pawns. The destination square is denoted by a combination of a letter (a-h) representing the column and a number (1-8) representing the row on the chessboard. Additional symbols may be used to indicate specific move types. The symbol "+" is used to indicate a check, while "#" denotes a checkmate. Castling moves are represented by "O-O" for kingside castling and "O-O-O" for queenside castling. PGN is a widely adopted format for recording chess games. It includes not only the SAN moves but also additional information like player names, event details, and game results. PGN files are human-readable and can be easily shared and analyzed. FEN is a notation system used to describe the state of a chess game. It represents the positions of pieces on the chessboard, active color, castling rights, en passant targets, and the half-move and full-move counters. The active color is represented by "w" for white or "b" for black.

When solving decision-making tasks, humans typically depend on information from two key sources: (1) Historical policy data, which provides interaction replay from the environment, and (2) Analytical insights in natural language form, exposing the invaluable thought process or strategic considerations. Despite this, the majority of preceding research focuses on only one source: they either use historical replay exclusively to directly learn policy or value functions, or engaged in language model training utilizing mere language corpus. In this paper, we argue that a powerful autonomous agent should cover both sources. Thus, we propose ChessGPT, a GPT model bridging policy learning and language modeling by integrating data from these two sources in Chess games. Specifically, we build a large-scale game and language dataset related to chess.

In this paper we apply model predictive control (MPC), rollout, and reinforcement learning (RL) methodologies to computer chess. We introduce a new architecture for move selection, within which available chess engines are used as components. One engine is used to provide position evaluations in an approximation in value space MPC/RL scheme, while a second engine is used as nominal opponent, to emulate or approximate the moves of the true opponent player. We show that our architecture improves substantially the performance of the position evaluation engine. In other words our architecture provides an additional layer of intelligence, on top of the intelligence of the engines on which it is based. This is true for any engine, regardless of its strength: top engines such as Stockfish and Komodo Dragon (of varying strengths), as well as weaker engines. Structurally, our basic architecture selects moves by a one-move lookahead search, with an intermediate move generated by a nominal opponent engine, and followed by a position evaluation by another chess engine. Simpler schemes that forego the use of the nominal opponent, also perform better than the position evaluator, but not quite by as much. More complex schemes, involving multistep lookahead, may also be used and generally tend to perform better as the length of the lookahead increases. Theoretically, our methodology relies on generic cost improvement properties and the superlinear convergence framework of Newton's method, which fundamentally underlies approximation in value space, and related MPC/RL and rollout/policy iteration schemes. A critical requirement of this framework is that the first lookahead step should be executed exactly. This fact has guided our architectural choices, and is apparently an important factor in improving the performance of even the best available chess engines.

A computer made from DNA that can solve basic chess and sudoku puzzles could one day, if scaled up, save vast amounts of energy over traditional computers when it comes to tasks like training artificial intelligence models. DNA devices have a number of potential advantages, such as being able to safely store vast amounts of information, in microscopically tiny volumes, for millennia.

Recent advancements in AI have sped up the evolution of versatile robot designs. Chess provides a standardized environment that allows for the evaluation of the influence of robot behaviors on human behavior. This article presents an open-source chess robot for humanrobot interaction (HRI) research, specifically focusing on verbal and non-verbal interactions. OpenChessRobot recognizes chess pieces using computer vision, executes moves, and interacts with the human player using voice and robotic gestures. We detail the software design, provide quantitative evaluations of the robot's efficacy and offer a guide for its reproducibility. Keywords: Artificial Intelligence, Chess, Human-robot Interaction, Open-source, Transfer Learning 1. Introduction Robots are becoming increasingly common across a variety of traditionally human-controlled domains. Examples range from automated mowers that maintain community lawns to robots in assembly lines and agricultural settings. Recent scientific advancements in AI have enabled new opportunities for intelligent sensing, reasoning, and acting by robots. In particular, the rapid development of large language models, such as ChatGPT, and vision-language models, have lowered the barrier of human-to-robot communication by being able to transform text and images into interpretable actions or vice versa. As technology advances, it is likely that robots will attain greater capabilities and will be able to tackle tasks previously within the exclusive realm of human expertise. This ongoing evolution may also lead to closer and more productive interactions between humans and robots. At the same time, integrating different AI-based robotic components remains a challenge, and the human-robot interaction (HRI) field lags in terms of endorsing reproducibility principles (Gunes et al., 2022). Encouraging transparent and reproducible research, therefore, remains an ongoing task. Furthermore, chess has played an important role in advancing the field of AI, starting with Claude Shannon's chess-playing algorithm (Shannon, 1950) to the success of IBM's Deep Blue (Campbell et al., 2002) and DeepMind's self-play learning algorithm (Silver et al., 2018). In this paper, we incorporate modern AI algorithms into the design of a chess-playing robot to be used for studying HRI. HRI research may benefit from a chess-based setup because the game of chess provides a controlled rule-based environment in which the impact of robots on human players can be precisely measured.

Games have long played a role in AI research, both as a test-bed, and as a moving goal-post, constantly driving innovation. From the heyday of chess agents, when Deep Blue beat Gary Kasparov, to more recent advances, like AlphaGo's dark horse ascent to fame, games have both assisted AI research and provided something to aim for. As the AIs got better, the games they were applied to also got more complex. New game mechanics, such as the fog of war in StarCraft and the stochasticity of Poker, pushed researchers to adapt their methods to ever greater generality. In this paper, we argue that the deck-building strategy game Dominion [1] deserves to join the ranks of AI benchmark games, providing an RL-based bot in service of that benchmark. Dominion has all of the abovementioned elements, but it also incorporates a mechanic that is not present in other popular RL benchmarks: every game is played with a different set of cards. Since each dominion card has a specific rule printed on it, and the set of 10 cards for a game are randomly picked from among hundreds of cards, no two games of Dominion can be approached the same way. Thus a key part of playing Dominion is adapting one's inductive bias of how to play to the specific cards on the table.

In the world of brain-computer interfaces, it can seem as if one company sucks up all the oxygen in the room. Last month, Neuralink posted a video to X showing the first human subject to receive its brain implant, which will be named Telepathy. The recipient, a 29-year-old man who is paralyzed from the shoulders down, played computer chess, moving the cursor around with his mind. Neuralink's announcement of a first-in-human trial made a big splash not because of what the man was able to accomplish--scientists demonstrated using a brain implant to move a cursor in 2006--but because the technology is so advanced. But Neuralink isn't the only company developing brain-computer interfaces to help people who have lost the ability to move or speak.

The original version of this story appeared in Quanta Magazine. When Covid-19 sent people home in early 2020, the computer scientist Tom Zahavy rediscovered chess. He had played as a kid and had recently read Garry Kasparov's Deep Thinking, a memoir of the grandmaster's 1997 matches against IBM's chess-playing computer, Deep Blue. He watched chess videos on YouTube and The Queen's Gambit on Netflix. Despite his renewed interest, Zahavy wasn't looking for ways to improve his game.

We propose a neural network-based approach to calculate the value of a chess square-piece combination. Our model takes a triplet (Color, Piece, Square) as an input and calculates a value that measures the advantage/disadvantage of having this piece on this square. Our methods build on recent advances in chess AI, and can accurately assess the worth of positions in a game of chess. The conventional approach assigns fixed values to pieces $(\symking=\infty, \symqueen=9, \symrook=5, \symbishop=3, \symknight=3, \sympawn=1)$. We enhance this analysis by introducing marginal valuations. We use deep Q-learning to estimate the parameters of our model. We demonstrate our method by examining the positioning of Knights and Bishops, and also provide valuable insights into the valuation of pawns. Finally, we conclude by suggesting potential avenues for future research.

We're more distracted than ever. Why remember anything when I can just Google it? Why summon the attention to read a book when I can just scroll through Twitter? Some philosophers believe that ChatGPT and its siblings will further diminish our ability to do the kind of "deep work" needed to spark creativity and breed big ideas. What good are the tools if we begin to rely on them so much that we no longer have the capacity to think bigger?