Computer Chess Research Articles

Turing tests in chess: An experiment revealing the role of human subjectivity

With the growing capabilities of AI, technology is increasingly able to match or even surpass human performance. In the current study, focused on the game of chess, we investigated whether chess players could distinguish whether they were playing against a human or a computer, and how they achieved this. A total of 24 chess players each played eight 5 + 0 Blitz games from different starting positions. They played against (1) a human, (2) Maia, a neural network-based chess engine trained to play in a human-like manner, (3) Stockfish 16, the best chess engine available, downgraded to play at a lower level, and (4) Stockfish 16 at its maximal level. The opponent’s move time was fixed at 10 s. During the game, participants verbalized their thoughts, and after each game, they indicated by means of a questionnaire whether they thought they had played against a human or a machine and if there were particular moves that revealed the nature of the opponent. The results showed that Stockfish at the highest level was usually correctly identified as an engine, while Maia was often incorrectly identified as a human. The moves of the downgraded Stockfish were relatively often labeled as ‘strange’ by the participants. In conclusion, the Turing test, as applied here in a domain where computers can perform superhumanly, is essentially a test of whether the chess computer can devise suboptimal moves that correspond to human moves, and not necessarily a test of computer intelligence.

2024 world computer chess championships: The end of an era 1974–2024

After 50 years, the final World Computer Chess Championships will be held in Santiago de Compostela, Spain, October 19–24, 2024. Most of the world’s strongest programs will be competing for the coveted Shannon Trophy. In addition to the tournaments, there will be a very special event: the Chess: History, Experiments, and Search Symposium (CHESS), which will bring together many of the pioneers of computer chess.

Artificial Intelligence and Expert Systems

Artificial Intelligence (AI) is a specialized field of computer science focused on creating machines that can emulate human intelligence and behaviour. The term "Artificial Intelligence" was first introduced by John McCarthy in 1956 at the Massachusetts Institute of Technology (MIT) in the USA. AI encompasses a variety of applications including game playing, expert systems, natural language processing, neural networks, and robotics. As of now, no computer systems have achieved true artificial general intelligence, which would enable them to perform any intellectual task that a human can. However, significant progress has been made in specific domains, particularly in game playing. Modern computer chess programs, for instance, have surpassed human players in terms of skill and performance. In the early 1980s, expert systems were heralded as the future of AI and computing. These systems are designed to mimic the decision-making abilities of human experts in specialized fields such as medicine and engineering. Despite their potential, expert systems have not fully met the high expectations set for them. They tend to be costly to develop and maintain, and their utility is often limited to specific, well-defined tasks. Currently, neural networks represent one of the most dynamic and rapidly advancing areas of AI. These algorithms, inspired by the structure of the human brain, have demonstrated success in various applications, including voice recognition and natural language processing. When it comes to programming AI applications, LISP (List Processing) and Prelog (Programming in Logic) are two of the most widely used languages due to their suitability and flexibility for AI-related tasks.

Development of computer technologies in chess

The purpose of the work: to determine ways of using computer technologies to optimize the process of training chess players. Methodology: analysis and generalization of data from scientific and methodical literature, programmatic and normative documents, pedagogical observation. Scientific novelty: computer technology has become an integral part of the chess game. Computer programs for playing chess have become more modern and professional, and can be used to prepare for matches, analyze games and improve the level of play. Computer databases of chess games, such as "Chess Base" and "Chess Assistant" accumulate all the accumulated practical experience of the sport and currently contain millions of games, being supplemented with new items every week, allowing searching by many parameters. The mover reports the best moves in each position and scores each move. These engines are perfect enough that even beginners can immediately tell when world-class players are making mistakes. Conclusions: Technology has changed the game of chess, improving the process of learning, training and improvement. At this time, there is chess software that is available for students of all levels, from beginners to masters. Programs allow you to effectively implement many functions of collection, systematization, storage of chess information, tactical analysis of selected positions of the highest quality at a level inaccessible to humans. The intensity of classes with the solution of training and control tasks, with the verification of various branches and options, with an emphasis on the most important subtleties in these branches, has increased. The possibilities of submitting educational material, selection according to the level of complexity and thematic focus have expanded. Computer programs in chess allow you to train skills and analyze games, which makes it possible to improve your game and achieve better results.

Hans Berliner: A life in chess, computer chess and much more

Hans Berliner: A life in chess, computer chess and much more

The future of computer chess tournaments

The future of computer chess tournaments

From beginner to over champion

In the game of chess the notion of beginner has three meanings. In this contribution they are used as a framework for the history of computer chess. The natural line of reasoning will then bring us to main question: Will the game of Chess (soon) be Solved? A positive answer will certainly be seen as a stepping stone for the next question: Can a computer play perfect chess? Of course, this question will have a natural follow-up in the form of an even more challenging question: Is perfect thinking by a computer possible? The audacious answer “yes” will lead to the world-wide prevailing question of all current AI scientists: Will our intelligence be replaced by AI? The article finds its way (not discussed by us) via machine learning and autonomous adaptation to deep learning and thereafter via Explainable AI and Interpretable AI to solving the Game of Chess. Cornerstones of this historical development will be given in the next three articles by Richard Pijl, Dap Hartmann and Jon Edwards.

Computer Chess: A Historical Perspective

Computer Chess: A Historical Perspective

“Intelligent Heuristics Are the Future of Computing”

Back in 1988, the partial game trees explored by computer chess programs were among the largest search structures in real-world computing. Because the game tree is too large to be fully evaluated, chess programs must make heuristic strategic decisions based on partial information, making it an illustrative subject for teaching AI search. In one of his lectures that year on AI search for games and puzzles, Professor Hans Berliner—a pioneer of computer chess programs 1 —stated: As a student in the field of the theory of computation, I was naturally perplexed but fascinated by this perspective. I had been trained to believe that “Algorithms and computational complexity theory are the foundation of computer science.” However, as it happens, my attempts to understand heuristics in computing have subsequently played a significant role in my career as a theoretical computer scientist. I have come to realize that Berliner’s postulation is a far-reaching worldview, particularly in the age of big, rich, complex, and multifaceted data and models, when computing has ubiquitous interactions with science, engineering, humanity, and society. In this article, 2 I will share some of my experiences on the subject of heuristics in computing, presenting examples of theoretical attempts to understand the behavior of heuristics on real data, as well as efforts to design practical heuristics with desirable theoretical characterizations. My hope is that these theoretical insights from past heuristics—such as spectral partitioning, multilevel methods, evolutionary algorithms, and simplex methods—can shed light on and further inspire a deeper understanding of the current and future techniques in AI and data mining.

Computer Chess, Circa 1920

Computer Chess, Circa 1920

50, 100 & 150 Years Ago: Computer chess master; private bathing for birds.

50, 100 & 150 Years Ago: Computer chess master; private bathing for birds.

The 2022 World Computer Chess Championships

After two years of pandemic-related cancellations, the World Computer Chess Championship returned in July 2022. The title of World Computer Chess Champion, first contested in 1974, was successfully defended by Komodo. The World Chess Software Championship was won by Ginkgo. The World Computer Speed Chess Championship title was shared by Leela Chess Zero and Ginkgo.

Training with AI: Evidence from chess computers

AbstractWe suggest that AI can help decision‐makers learn; specifically, that it can help them learn strategic interactions by serving as artificial training partners and thus help them to overcome a bottleneck of scarce human training partners. We present evidence from chess computers, the first widespread incarnation of AI. Leveraging the staggered diffusion of chess computers, we find that they did indeed help chess players improve by serving as a substitute for scarce human training partners. We also illustrate that chess computers were not a perfect substitute, as players training with them were not exposed to and thus did not learn to exploit idiosyncratic (“human”) mistakes. We discuss implications for research on learning, on AI in management and strategy, and on competitive advantage.

Chess Software based on AI

Chess, which is played between two players on a board, is an intellectual and mental game. Its unique rules of play help to better the player's mental and intellectual activities, and it has a huge following of players all over the world who are keen to play it. This document discusses the completely computerized chess game. The game is first computerized so that two players can play chess on a computer using all of the current rules. Second, the AI program was used to create the game, adding the computer's intelligence to make it more compelling for players to play directly against it.

How Beneficial or Threatening is Artificial Intelligence?

Computer scientists have studied artificial intelligence and machine learning since computers were invented. There have been three waves of developments, in this field. First development, in the 1950s, computer chess and reversiTM, were realized. In the second development, “expert systems” attracted attention and started the “5th Generation Computer Project.” in 1980s. However, the results of both the developments were insufficient (for example, 5th Generation Computer was not created), and the enthusiasm for expecting new type of computers such as artificial intelligence quickly diminished. We are now in the third wave of the AI developments. Will it produce satisfactory results? Will it revolutionize life sciences? In this article, I have tried to answer these questions.

Research Paper on Artificial Intelligence

This branch of computer science is concerned with making computers behave like humans. Artificial intelligence includes game playing, expert systems, neural networks, natural language, and robotics. Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and natural-language processing. There are several programming languages that are known as AI languages because they are used almost exclusively for AI applications. The two most common are LISP and Prolog. Artificial intelligence is working a lot in decreasing human effort but with less growth.

Characterization of precipitates susceptible to clogging of sump filters after a Loss-of-Coolant-Accident in PWRs

During a Loss Of Coolant Accident involving a primary circuit failure in a nuclear power plant, debris, generated by the impact of the steam jet, is transported to the hot liquid phase formed at the bottom of the nuclear containment. Water is recirculated after passing through filters. Chemicals are released and may precipitate on the filters, increasing the pressure drop. This paper investigates the characterization of precipitate formation in a soda/borate buffer, due to the presence of calcium, silicon, and zinc, released from mineral fibers, concrete particles, or the corrosion of metal surfaces. Experiments run at various temperatures and chemical compositions are presented, with the objective of identifying the precipitates and determining the critical thresholds of concentration and temperature beyond which the risk of chemical clogging disappears. The experimental results are compared to those calculated with the CHESS software, intended to calculate the speciation at thermodynamic equilibrium. The results show the formation of amorphous precipitates: silica, calcium borates, borosilicates and zinc silicate. Precipitation is temperature dependent, with the highest masses obtained between 50 and 60 °C. The comparison of experimental and calculated results indicates a good overall consistency. This work demonstrates the importance of considering chemical clogging in this context.

Tree Search Algorithms For Chinese Chess

Computer Games are an important aspect of Artificial Intelligence (AI) which has produced many representative algorithms of optimal strategies. Therefore, many AI researchers have applied game theory to various board games in an attempt to find the essence of games and grasp the core of AI behaviour. AlphaGo is a very famous and typical chess-playing program that uses Monte Carlo tree search (MCTS) to estimate the value of each node in the search tree and optimize the possible results. Chinese chess is one of the traditional chess versions with its typical strategy. However, due to its late start and high complexity, there are many more technical problems to be solved. In this paper, we recommend two commonly used search frameworks for Chinese chess: the minimax algorithm and the alpha-beta pruning algorithm. On this basis, to deal with the complex and changeable situation, the Chinese chess algorithm based on Monte Carlo tree search and appropriate neural network evaluation function is studied.

Ways to improve chess training

With the development of modern technologies, chess sports are becoming more and more popular every day. Chess contributes to the development of concentration of attention, the ability to overcome difficulties, to plan their own actions, to think logically. They are also used to model artificial intelligence systems. As the game of chess manifests the qualities of the human person, they are used as a model for scientific research in psychology and pedagogy. The system of chess classes, identifying and developing personal abilities, forming a progressive orientation of the personality, contributes to the general development and education of the younger generation. Study of possibility of improvement of training process in chess sport on the basis of targeted study of professionally significant intellectual functions of chess players - one of worthy tasks of specialists in the field of medicine, regardless of specialization. They are also used to model artificial intelligence systems. The training of chess players and continuous improvement require the attention of athletes and coaches. Only with the right approach to the training process can an effective result be possible. The use of modern computer technology greatly expands the trainer’s ability to train athletes. With the use of chess computer programs, it becomes possible to carry out more detailed information-retrieval, training and play functions, develop skills and skills of chess skill athlete at all stages of the chess game. Also, their use will help to carry out the optimal search for sparring partners on game portals, to conduct distance training and get operational information. The article describes the classification of chess players by rating, ranks and ranks. Also considered the training of chess players in modern realities. Modern trainers and chess players in the constant search for new effective chess books and computer programs to improve the technique of training.

Better Problems

Better Problems