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.

The chess domain is well-suited for creating an artificial intelligence (AI) system that mimics real-world challenges, including decision-making. Throughout the years, minimal attention has been paid to investigating insights derived from unstructured chess data sources. In this study, we examine the complicated relationships between multiple referenced moves in a chess-teaching textbook, and propose a novel method designed to encapsulate chess knowledge derived from move-action phrases. This study investigates the feasibility of using a modified sentiment analysis method as a means for evaluating chess moves based on text. Our proposed Aspect-Based Sentiment Analysis (ABSA) method represents an advancement in evaluating the sentiment associated with referenced chess moves. By extracting insights from move-action phrases, our approach aims to provide a more fine-grained and contextually aware `chess move'-based sentiment classification. Through empirical experiments and analysis, we evaluate the performance of our fine-tuned ABSA model, presenting results that confirm the efficiency of our approach in advancing aspect-based sentiment classification within the chess domain. This research contributes to the area of game-playing by machines and shows the practical applicability of leveraging NLP techniques to understand the context of strategic games.

Working from home increases your risk of making mistakes, a study examining the quality of chess play has found. The standard was significantly worse when players competed online instead of face to face, researchers discovered, suggesting that not being in the office is harmful to productivity. They monitored nearly 215,000 chess moves made by players during in-person and digital tournaments, checking them against what was the best play by using artificial intelligence. Such was the impact on performance when playing remotely, it would have taken Norwegian grandmaster Magnus Carlsen, the world's top-rated player, to the same rating as the current 20th-best player, according to Dainis Zegners from Rotterdam School of Management, one of the study's co-authors. He said the research showed that remote working could hinder people's ability to carry out mentally-intense tasks while alone.

Since IBM's Deep Blue defeated World Chess Champion Garry Kasparov in their 1997 match, chess engines have only increased dramatically in strength and understanding. Today, the best chess engines are an almost incomprehensible 1,000 Elo points stronger than Deep Blue was at that time. A quick Google search for terms such as "Magnus Carlsen versus Stockfish" turns up numerous threads asking if humans can compete against today's top chess engines. The broad consensus seems to be that the very best humans might secure a few draws with the white pieces, but in general, they would lose the vast majority of games and would have no hope of winning any games. I see no reason to disagree with this consensus. Despite the clear superiority of engines, there ARE positions which chess engines don't (and possibly can't) understand that are quite comprehensible for human players.

We conduct the first study of its kind to generate and evaluate vector representations for chess pieces. In particular, we uncover the latent structure of chess pieces and moves, as well as predict chess moves from chess positions. We share preliminary results which anticipate our ongoing work on a neural network architecture that learns these embeddings directly from supervised feedback. The fundamental challenge for machine learning based chess programs is to learn the mapping between chess positions and optimal moves [5, 3, 7]. A chess position is a description of where pieces are located on the chessboard. In learning, chess positions are typically represented as bitboard representations [1]. A bitboard is a 8 8 binary matrix, same dimensions as the chessboard, and each bitboard is associated with a particular piece type (e.g.

By Ateesh TankhaConsider the following bit of reasoning: Grandmaster Garry Kasparov can anticipate any chess move by any man. Artificial intelligence (AI) can anticipate any chess move by Kasparov. So, AI can anticipate any chess move by any man. This argument holds as long as it refers only to the game of chess. The moment'any move' refers to something else -- say, a malafide intention to commit fraud -- all bets are off.

My dad taught me when I was young, but I guess he was one of those dads who always let their kid win. To compensate for this lack of skill in one of the world's most popular games, I did what any data science lover would do: build an AI to beat the people I couldn't beat. But I wanted to see how a chess engine would do without reinforcement learning as well as learn how to deploy a deep learning model to the web. FICS has a database of 300 million games, individual moves made, the results, and the rating of the players involved. I downloaded all the games in 2012 where at least one player was above 2000 ELO.

Will robots one day destroy us? For developments in artificial intelligence (AI) -- machines programmed to perform tasks that normally require human intelligence -- are poised to reshape our workplace and leisure time dramatically. This year, a leading Oxford academic, Professor Michael Wooldridge, warned MPs that AI could go'rogue', that machines might become so complex that the engineers who create them will no longer understand them or be able to predict how they function. AlphaZero taught itself chess in just four hours and thrashed a grandmaster using moves never seen before in the game's 1,500 year history Yes, it's a concern, but a'historic' new development makes unpredictable decisions by AI machines the least of our worries. And it all started with a game of chess. AlphaZero, an AI computer program, this month proved itself to be the world's greatest ever chess champion, thrashing a previous title-holder, another AI system called Stockfish 8, in a 100-game marathon.