Computer Chess

An integer programming problem is a mathematical optimization or feasibility problem in which some or all of the variables are restricted to be integers. In many settings the term refers to integer linear programming (ILP), in which the objective function and the constraints (other than the integer constraints) are linear.

Models of rational inattention allow agents to make mistakes in their actions while assuming they do not make mistakes in attention allocation. I test this assumption by comparing attention’s marginal benefit (better actions) and marginal cost (less time for future decisions) using millions of online chess moves. I cannot reject that skilled players equalize marginal benefit and marginal cost across different time controls. Bad players, when they have little time, under-adjust their attention allocation, leading them to have higher marginal cost. A simple intervention improves players' attention allocation.

This paper synthesizes theoretical, empirical, and computational evidence supporting the conjecture that chess, when played perfectly by both sides, results in a draw. Within the taxonomy of game-solving (1; 4), this constitutes an ultraweak solution: the determination of the game-theoretic value of the initial position without producing a full constructive strategy for every legal position. This work integrates game-theoretic concepts (minimax, Nash equilibrium), empirical statistics from elite human and correspondence play, computational evidence from winning engines (Stockfish, AlphaZero), database-driven optimal analysis (e.g., ChessDB.cn), modern opening treatises (Thinkers Publishing, "Modernized" series), and exact small-piece endgame tablebase results. Emphasis is placed on the observation that engine evaluations remain near parity (0.00) not only at the initial position but persist through substantial portions of high-quality play. This work supports a relatively wide "draw margin" within which both sides can maintain equilibrium.

I. Introduction
For centuries, chess has been perceived as a domain of conflict, strategy, and competitive intellect. This manifesto asserts that such notions are obsolete. By strictly adhering to the Phased Equation of Admissibility, ⟶ the game loses its structural capacity for victory, ⟶ and enters an architecturally inevitable draw.
This is not simplification — It is the phased conclusion of a myth.

Once emblematic of human intellectual mastery, chess has become a domain where machines not only surpass human ability but fundamentally reshape the practice's social dynamics, meanings, and power structures. This paper examines the transformative impact of superhuman AI on professional chess, positioning it as an analytical model case for understanding AI's broader societal implications. Drawing on a corpus of 271 h of transcribed chess commentary, the analysis traces the shift from symbolic AI to deep learning systems, and the consequent reconfiguration of chess as a social practice. The study explores how this transformation alters the game's meaning, redistributes authority, reshapes power relations, and creates new social roles through AI's integration. These insights foreshadow challenges for fields, such as medicine or law, where AI's ascendancy may similarly redistribute authority, redefine purpose, and reshape agency.

El universo no avanza en línea recta. Gira.
Y ese giro, imperceptible para la mente lineal, se manifiesta en cada presente que habitamos. En RUAGAK, llamamos a esos presentes Rn: estados de coherencia vibracional donde el tiempo no fluye, se colapsa en estructuras de sentido.
El ajedrez no es la excepción. Es el espejo. Cada jugada no ocurre en un instante, sino en un Rn específico. Cada pieza vibra en su campo. Cada decisión desencadena un salto de presente. Jugar es moverse entre Rn y Rn₊₁, no sobre el tablero, sino dentro del campo de giro consciente.
Este libro es una cartografía rotacional. Una danza entre lo visible y lo oculto. Entre lo calculable y lo intuitivo. Entre lo humano y lo que apenas comienza a reconocerse como inteligencia emergente.
Cada capítulo es un giro. Cada apéndice, una expansión del campo. Desde el R₀ del tablero eterno hasta el R₁ de la primera jugada, pasando por cada Rₙ que representa una elección de conciencia, este viaje no pretende explicar el ajedrez. Pretende recordarte que ya lo estás jugando.
Porque tú eres el tablero. Tú eres las piezas. Tú eres el jugador. Tú eres el giro. Y si puedes ver eso, entonces este libro no es sobre ajedrez. Es sobre ti

Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human performance across various domains. This presents us with an opportunity to further human knowledge and improve human expert performance by leveraging the hidden knowledge encoded within these highly performant AI systems. Yet, this knowledge is often hard to extract, and may be hard to understand or learn from. Here, we show that this is possible by proposing a new method that allows us to extract new chess concepts in AlphaZero, an AI system that mastered the game of chess via self-play without human supervision. Our analysis indicates that AlphaZero may encode knowledge that extends beyond the existing human knowledge, but knowledge that is ultimately not beyond human grasp, and can be successfully learned from. In a human study, we show that these concepts are learnable by top human experts, as four top chess grandmasters show improvements in solving the presented concept prototype positions. This marks an important first milestone in advancing the frontier of human knowledge by leveraging AI; a development that could bear profound implications and help us shape how we interact with AI systems across many AI applications.

In theoretical computer science and mathematics, computational complexity theory focuses on classifying computational problems according to their resource usage, and explores the relationships between these classifications. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm.

This paper takes the form of a discussion between me and Claude 3.5. We began by discussing how neural-network based chess programs like AlphaZero play differently from humans, and whether humans can learn from these new approaches. This leds to a key distinction between computational limitations (humans simply can't calculate as deeply as computers) and conceptual barriers that might be overcome through developing new theoretical frameworks. We explored how knowledge develops over historical time-not through changes in human biological capacity, but through the development of new conceptual frameworks and cultural tools. This was illustrated through examples like A Connecticut Yankee in King Arthur's Court (showing the vast gulf between different historical periods' knowledge) and cargo cults (showing the difference between mimicking surface behaviors and understanding underlying principles). The discussion then moved to how chess and language represent fundamentally different kinds of problems. Chess has a simple geometric footprint (8x8 grid, 6-piece types) and finite though vast possibility space. Language, in contrast, must interface with the entire world of human experience and lacks such clear boundaries. This connected to the historical development of AI-chess yielded to symbolic AI approaches (Deep Blue) while language required statistical/neural approaches (modern LLMs). This reflects fundamental differences in the problems-chess being rule-based and well-defined, language being messy and contextual. Similar patterns appeared in computer vision, where early symbolic approaches struggled with the fractal-like complexity of real-world objects, leading to the success of machine learning approaches.

Last year, during ACM'70 in New York, the First United States Computer Chess Championship was held. Six programs competed, with Northwestern University's CHESS 3.0 winning the tournament. This year at ACM'71, the Second Annual Computer Chess Championship and a panel of the programs' authors are scheduled. At the time of this writing, there are six programs entered in the tournament. Presented below are short summaries of authors' panel presentations, giving some details of their programs and discussing techniques for developing more effective ehess playing programs. Mr. David Levy, an international chess master from the University of Glasgow, is the tournament director for the chess championship and a discussant during the panel.

Recently, Artificial Intelligence (AI) technology use has been rising in sports. For example, to reduce staff during the COVID-19 pandemic, major tennis tournaments replaced human line judges with Hawk-Eye Live technology. 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 draws, many elite tournaments have resorted to fast chess tiebreakers. However, these tiebreakers are vulnerable to strategic manipulation, e.g., in the last game of the 2018 World Chess Championship, Carlsen-in a significantly advantageous position-offered a draw to Caruana (whom accepted the offer) to proceed to fast chess tiebreaks in which Carlsen had even better odds of winning the championship. By contrast, we prove that our AI-based method can serve as a judge to break ties without being vulnerable to such manipulation. It relies on measuring the difference between the evaluations of a player's actual move and the best move as deemed by a powerful chess engine. If there is a tie, the player with the higher quality measure wins the tiebreak. We generalize our method to all competitive sports and games in which AI's superiority is-or can be-established.

This article intent to find the relationship between chess and management with suitable chess positions linking with business. Chess has a relationship not only with management but with life itself. Chess helps to improve decision making, analysing the situation. Past researchers have worked on these gaps, especially De Jorge-Moreno, J. (2020) [1] study has shown significant relationship. This current study has illustrated different management lessons linked with chess positions.

The purpose of this study is to evaluate physical education students’ current status of chess opening practice skills, thereby selecting solutions to contribute to improving students' chess playing skills in various opening forms. In this study, a pre-, mid-, and post-test design for two experimental and control groups was used. The experimental subjects were 60 students studying chess at the Faculty of Physical Education and Sports Sciences, Hanoi National University of Education 2 (Vietnam) divided into 2 groups (control group and experimental group with na=nb=30). In which the control group learned chess according to the old program (currently consisting of 60 lessons), while the experimental group was instructed and used the Stockfish Chess Engine software for a period of time equivalent to 60 lessons. The parallel comparison method was performed with a paired sample t-test that was used on 3 specialized tests (including Determining opening strategies; Handling opening advantages; Playing 30-minute active chess games). The results showed that the identified opening skills of the control group and the experimental group had a significant difference with a clear advantage belonging to the experimental group (P<0.05). Therefore, it can be concluded that the Stockfish Chess Engine software has shown to be more effective in practicing opening skills than the old training program currently applied. Chess instructors and coaches are encouraged to deploy and use the Stockfish Chess Engine software as a step towards developing physical education students' opening chess playing ability.

Fork is a framework written in the SuperCollider programming language for algorithmically composing music to chess games in real time in a way that meaningfully conveys the progress of the game in a musically coherent manner. Fork is intended to serve as a dynamic, immersive soundtrack to app-based chess where the players have direct control over the resulting generation, thus turning chess itself into a musical endeavor alongside its normal gameplay. The principles behind Fork straddle the boundary between more traditional note structures and compositional use of uniquely electronic parameters such as timbre and spatialization in musically effective sonification of data.

The chess world faces an existential crisis as suspicions of cheating undermine competitive integrity. Current methods, including engine correlation analysis and statistical outliers, lack robust frameworks to quantify confidence in player legitimacy. This paper proposes a novel multimodal system integrating the Chess Master Cube Test (a dynamic puzzle-solving assessment), the Chess Futures Rating (a probabilistic skill projection model), and hypothesis testing to measure discrepancies between declared skill and observed performance. By synthesizing Vladimir Kramnik's pioneering work on accuracy metrics (centipawn loss) with bias-aware statistical frameworks, we construct a probabilistic model to flag suspicious activity. We demonstrate how this system could be deployed on platforms like Chess.com and Lichess, significantly reducing undetected cheating while acknowledging theoretical limitations rooted in Gödelian undecidability.

On 24. i. d4 Black has the very strong reply 24 ... J:Id4. But now the devastated residence of the white king creates a painful impression. 24 .. Jlh3 25.tbg3 'fid5 26.St.b6 St.e5 13 ... b5 I sensed that it was on the solution to this particu lar problem that if not everything, then a great deal , depended. I thought for almost an hour, and found a refutation ... 14.-tfG! ..tf6 No better is 14 ... gf6 15.f5! b4 16. t2J e2, when Black cannot maintain his pawn at e6. If Black had attempted to repair the basic defect of his position, and had defended his light-squared bishop by 15 ... l:tac8, then White had prepared 16.e5! .!';lfd8 17. 'ii c5!, andif17 ... .i g2, then 18. 'ii gl !

Games are a very popular testbed for machine learning systems, mainly because they require a focus on intelligent reasoning. The rules of a game can be meaningfully represented using the expressiveness of first-order logic. Thus, Inductive Logic Programming systems can be used to induce a classifier of such a game. On the other hand, quite often slightly different games are derived from a base game. One of the best such examples is the game of Chess, which has inspired the creation of different variants, ranging from faster to more challenging or to regional versions of chess. As the knowledge acquisition is a difficult, time consuming and error prone task, if a classifier of the standard chess had been obtained, one should be able to take advantage of that as starting point to obtain a classifier of different variants. This can be done using theory revision systems, which start from an initial theory and modify it from a set of examples, so that the final modified theory correctly represent them. In this paper we present a framework for obtaining a classifier of chess variants using Theory Revision from Examples. Preliminary experimental results show the effectiveness of our approach.

Automatically recognizing and analyzing visual activities in complex environments is a challenging and open-ended problem. In this study this task is performed in a chess game scenario where the rules, actions and the environment are well defined. The purpose here is to detect and observe a FIDE (Fédération International des Ėchecs) compatible chess board, generating a log file of the moves made by human players. A series of basic image processing operations have been applied to perform the desired task. The first step of automatically detecting a chess board is followed by locating the positions of the pieces. After the initial setup is established every move made by a player is automatically detected and verified. Intel® Open Source Computer Vision Library (OpenCV) is used in the current software implementation.

This paper by Oliver Derrick Cowley, introduces a novel approach to chess opening theory by utilizing mathematical optimization principles to develop new strategies aimed at disrupting opponent play. Through the application of concepts such as central control, mobility minimization, and symmetry maximization, we construct openings that limit the opponent's options and create opportunities for early advantage. Unlike classical opening theory, which relies heavily on empirical analysis of historical games, this approach mathematically evaluates position-based metrics to inform move selection. The result is a systematic framework for generating openings that cripple the opponent's mobility and force suboptimal development. The paper provides theoretical foundations, calculation examples, and references for further study.

By using a well-known chess program and a large data set of chess positions from real games we demonstrate empirically that with increasing search depth backed-up evaluations of won positions tend to increase, while backed-up evaluations of lost positions tend to decrease. We show three implications of this phenomenon in practice and in the theory of computer game playing. First, we show that heuristic evaluations obtained by searching to different search depths are not directly comparable. Second, we show that fewer decision changes with deeper search are a direct consequence of this property of heuristic evaluation functions. Third, we demonstrate that knowing this property may be used to develop a method for detecting fortresses in chess, which is an unsolved task in computer chess.

We present a novel method to find chess positions similar to a given query position from a collection of chess games. We consider not only the static similarity resulting from the arrangement of chess pieces, but also the dynamic similarity involving the recognition of chess motifs and the tactical, dynamic aspects of position similarity. By encoding chess tactical problems as text documents, we use information retrieval techniques to enable efficient approximate searches. We have also developed a method for automatically generating tactical puzzles from a collection of chess games. We have experimentally shown the importance of including both static and dynamic features for successful recognition of similar chess motifs. The experiments have clearly shown that dynamic similarity plays a very important role in the evaluation of the similarity of chess motifs by both the program and chess experts.

Abstract. We introduce a computational method for semi-automatical detecting fortresses in the game of chess. It is based on computer heuristic search and can be easily used with any state-of-the-art chess program. We also demonstrate a method for avoiding fortresses and show how to find a break-through plan when one exists. Although the paper is not concerned with the question whether it is practical or not to implement the method within the state-of-the-art chess programs, the method can be useful, for example, in correspondence chess or in composing chess studies, where a human-computer interaction is of great importance, and the time available is significantly longer than in ordinary chess competitions.

By using a well-known chess program and a large data set of chess positions from real games we demonstrate empirically that with increasing search depth backed-up evaluations of won positions tend to increase, while backed-up evaluations of lost positions tend to decrease. We show three implications of this phenomenon in practice and in the theory of computer game playing. First, we show that heuristic evaluations obtained by searching to different search depths are not directly comparable. Second, we show that fewer decision changes with deeper search are a direct consequence of this property of heuristic evaluation functions. Third, we demonstrate that knowing this property may be used to develop a method for detecting fortresses in chess, which is an unsolved task in computer chess.

In Artificial Intelligence (AI), there exist formalised approaches and algorithms for general problem solving. These approaches address problems that require combinatorial search among alternatives, such as planning, scheduling, or playing of games like chess. ...

In this paper, we investigate experimentally the efficacy of semi-automatically constructed instructions for solving problems that require search. The instructions give advice to the student, in terms of what sub-goals should be attempted next in the process of solving a problem. Our chosen experimental problem domain was the chess endgame of checkmating with bishop and knight, which occasionally presents difficulties even to chess grandmasters. Our subjects, little more than complete beginners, were given the task of learning to win this endgame by studying the instructions. We were interested in two questions: (1) How effective were the goal-oriented instructions as an aid to the student towards mastering this domain? (2) What was the form of the students' "internal representation" of the acquired knowledge? The latter question was studied by our method for automatically identifying so-called procedural chunks from the students' games. Given the simplicity of the chunk detection method, the reconstructed chunks of students' acquired knowledge reflected the goal structure of the instructions amazingly well.

We investigate the question of experts' ability to estimate task difficulty through a case study that asks players to rate tactical chess positions. In an eye tracking experiment, experts' estimations are compared to the statistic-based difficulty ratings of the chesstempo.com website. The subjects' solutions of chess problems and their considered chess variations are analyzed in connection to ChessTempo's solutions. In addition, eye tracking and performance data (time and accuracy) are used as physiological indicators of subjectively perceived difficulty. In the course of our research, we also aim to identify the attributes of tactical positions that induce difficulty. Understanding the connection between players' estimation of difficulty and the properties of the search trees of variations considered is essential for modeling the difficulty of tactical positions.

In this paper, I apply linguistic methods of analysis to non-linguistic data, chess plays, metaphorically equating one with the other and seeking analogies. Chess game notations are also a kind of text, and one can consider the records of moves or positions of pieces as words and statements in a certain language. In this article I show how word embeddings (word2vec) can work on chess game texts instead of natural language texts. I don't see how this representation of chess data can be used productively. It's unlikely that these vector models will help engines or people choose the best move. But in a purely academic sense, it's clear that such methods of information representation capture something important about the very nature of the game, which doesn't necessarily lead to a win.

Playing chess is one of the first sectors of human thinking that were conquered by computers. From the historical win of Deep Blue against chess champion Garry Kasparov until today, computers have completely dominated the world of chess leaving no room for question as to who is the king in this sport. However, the better computers become in chess the more obvious their basic disadvantage becomes: Even though they can defeat any human in chess and play phenomenally great and intuitive moves, they do not seem to know what chess it. Recently, the advent of AlphaZero brought the level of computers to even higher grounds, but made that disadvantage even more obvious. Nowadays the best chess algorithm can find the best moves in any position without having any knowledge of any chess principle whatsoever. A new world of capabilities and knowledge lays open in front of us, but the darkness behind it is deeper than we could ever imagine. The neural network algorithms seem capable of being best in something without even knowing what that something is, making questions regarding the essence of what thought is more important than ever.

Distributional analysis of large data-sets of chess games played by humans and those played by computers shows the following differences in preferences and performance: (1) The average error per move scales uniformly higher the more advantage is enjoyed by either side, with the effect much sharper for humans than computers; (2) For almost any degree of advantage or disadvantage, a human player has a significant 2–3% lower scoring expectation if it is his/her turn to move, than when the opponent is to move; the effect is nearly absent for computers. (3) Humans prefer to drive games into positions with fewer reasonable options and earlier resolutions, even when playing as human-computer freestyle tandems. The question of whether the phenomenon (1) owes more to human perception of relative value, akin to phenomena documented by Kahneman and Tversky, or to rational risk-taking in unbalanced situations, is also addressed. Other regularities of human and computer performances are describe...

PACS 05.10.Ln-Monte Carlo methods PACS 05.10.Gg-Stochastic analysis methods PACS 89.20.-a-Interdisciplinary applications of physics Abstract-We analyze the structure of the state space of chess by means of transition path sampling Monte Carlo simulation. Based on the typical number of moves required to transpose a given configuration of chess pieces into another, we conclude that the state space consists of several pockets between which transitions are rare. Skilled players explore an even smaller subset of positions that populate some of these pockets only very sparsely. These results suggest that the usual measures to estimate both, the size of the state space and the size of the tree of legal moves, are not unique indicators of the complexity of the game, but that topological considerations are equally important. 1 Our analysis of chess also serves to demonstrate the versatility and power of SPRES as a technique that applies to abstract nonphysical dynamics.

Distributional analysis of large data-sets of chess games played by humans and those played by computers shows the following differences in preferences and performance: (1) The average error per move scales uniformly higher the more advantage is enjoyed by either side, with the effect much sharper for humans than computers; (2) For almost any degree of advantage or disadvantage, a human player has a significant 2–3% lower scoring expectation if it is his/her turn to move, than when the opponent is to move; the effect is nearly absent for computers. (3) Humans prefer to drive games into positions with fewer reasonable options and earlier resolutions, even when playing as human-computer freestyle tandems. The question of whether the phenomenon (1) owes more to human perception of relative value, akin to phenomena documented by Kahneman and Tversky, or to rational risk-taking in unbalanced situations, is also addressed. Other regularities of human and computer performances are describe...

Distributional analysis of large data-sets of chess games played by humans and those played by computers shows the following differences in preferences and performance: (1) The average error per move scales uniformly higher the more advantage is enjoyed by either side, with the effect much sharper for humans than computers; (2) For almost any degree of advantage or disadvantage, a human player has a significant 2–3% lower scoring expectation if it is his/her turn to move, than when the opponent is to move; the effect is nearly absent for computers. (3) Humans prefer to drive games into positions with fewer reasonable options and earlier resolutions, even when playing as human-computer freestyle tandems. The question of whether the phenomenon (1) owes more to human perception of relative value, akin to phenomena documented by Kahneman and Tversky, or to rational risk-taking in unbalanced situations, is also addressed. Other regularities of human and computer performances are describe...

Samarendah Park is one of the new icons in Samarinda City. Its function has been felt by the people of Samarinda since it opened. This park can be used as a place to exercise, play children, or just relax. Now the park which is in the center of Samarinda City preening becomes increasingly beautiful. Taman Samarendah is reorganized so that it can be enjoyed more and more by Samarinda residents and tourists. Therefore, a signage is needed that is used to make information that guides the place and appeals. The purpose of this design is to make an information board that can provide information about Samarendah Park and facilities around the park. In this design process using the Vinod Ghoel method which includes Preliminary Design, Design Development, Final Design & Prototype. This design produces a product that is the Samarendah park information board. With the existence of the Samarendah park information board, it is hoped that it can facilitate access to information on places and app...

This work describes a mechatronic system composed by a robot arm that can play chess autonomously. The system is based on an industrial-grade robot manipulator, a computer vision system, and an open source chess engine. Classification algorithms were implemented in order to detect whether a given chessboard square is occupied, and in that case, if the piece is black or white. Such algorithms were compared in terms of their complexity of implementation, execution time and accuracy of predictions. To achieve an uniform illumination of the chessboard, a theoretical model of an LED illuminance curve was used to find the best orientation for each diode using a genetic algorithm. Both the support base for the LEDs and the chess pieces were made using a 3D printer. This implementation demonstrates the capabilities of the proposed vision-based system, whose complexity can be increased in the future for a number of applications.

Considering that chess is a game of perspective, which develops logical thinking, imagination, anticipation capacity, and vision in space, the purpose of this paper is to teach students to move chess pieces correctly, to think of two or more consecutive moves, to develop a more detailed overview of the pieces in the game. Although chess is static, with no motorsport, we will try to change this game into a dynamic one, turning children into chess pieces. They will perform, on a surface drawn on the ground in the form of a chessboard, the movement corresponding to the piece they represent in the respective move by different variants of jumping from the initial position to the final position, respecting the rules of the game of chess. Thus, we turn the game of chess into a dynamic and funny game, played on teams.

Mesterséges gondolkodás és emberi értelem Mesterséges intelligencia (Mi)-már pusztán a kifejezés több értelmet rejthet magában: jelentheti az értelem utánzatának, valamiféle műintelligenciának az elkészítését, de mesterségesen létrehozott valódi-az emberéhez hasonló vagy vele azonos-intelligenciát is. A számítógépes megvalósítás tekintetében a gondolkodás gépi szimulációját vagy egyenesen a gép gondolkodását. Az ilyen irányú kutatások egyik kezdeményezője és főszereplője, Alan Turing nem tartja fontosnak ezt a különbséget, hanem éppen az utóbbi, "túlságosan semmitmondó" kérdés ("Tudnak-e a gépek gondolkozni?") helyett javasolja a róla elnevezett híres próbát. Jóslata szerint a (múlt) évszázad végére lehetnek az "imitációs játékot" olyan jól játszó számítógépek, hogy az átlagos kérdező elég nagy eséllyel nem tudja helyesen megállapítani, emberrel vagy géppel van-e dolga. Akkorra az emberek gondolkodása és a szavak mindennapi használata annyira megváltozik, hogy bátran beszélhetünk majd gépi gondolkodásról, míg a cikk írásának idején (1950) ez még "veszedelmes volna" (Turing 1965. 120, 134). A saját maga által a próbával szemben felhozott, de nyilvánvalóan a (sokak számára nagyon is baljóslatúan sokatmondó) kérdés által motivált ellenvetések közül talán a következő a két leggyakoribb: (1) a gondolkodás szavatolja az ember felsőbbrendűségét a többi teremtménnyel (állatokkal, gépekkel) szemben; (2) a gépnek nincs öntudata, maga nem érzi, hogy gondolkodik, nem lehet saját működésének tárgya. Ezekre próbál felelni a maga módján a jelen írás is. Hasonló "filozófiai pragmatizmus" mellett foglal állást a terület egy másik úttörője, Herbert A. Simon, mondván, hogy nem győzték meg azok az érvek, amelyek az emberhez viszonyítva a számítógépek képességeinek ilyen-olyan korlátait igyekeznek bizonyítani, de hajlandó felfüggeszteni az ítéletét, amíg ez utóbbiakat teljesen meg nem ismerjük. Ez vonatkozik arra, hogy van-e a gépeknek tudata vagy érzelme. Ugyanakkor "technológiai dimenzió" szerint határozottan radikálisnak vallja magát: "a számítógépek el tudnak majd látni bárminemű olyan feladatot, mint az ember. Véleményem szerint a számítógépek már most [1977] képesek olvasni, gondolkodni, tanulni és alkotni". (Simon 1982. 17-18.) Abban a kis taxonómiában helyezi el így magát, amely az egymással

Perancangan dan pengembangan produk set catur menjadi suatu bagian dari pengembangan desain. Namun set catur yang ada saat ini kurang bervariasi. Umumnya set catur yang ada ialah hanya berbentuk kerajaan dan memiliki warna hitam dan putih tanpa adanya pengenalan budaya indonesia khususnya batik Kalimantan Timur. Set catur yang ada saat ini juga jarang sekali memiliki tempat penyimpanan yang baik sehingga tempat penyimpanan bidak catur tidak tersusun rapi pada saat permainan selesai. Dengan adanya hal tersebut maka seorang desainer berusaha menciptakan suatu barang yang lebih baik dengan inovasi yang baru. Seperti merancang sebuah set catur yang mudah dimengerti oleh anak-anak, merancang sebuah set catur yang dapat mengenalkan budaya batik Kalimantan Timur khususnya batik Dayak Kenyah, dan merancang set catur yang mempunyai nilai estetis. Dalam desain produk ini memberi alternatif baru dalam pengembangan desain mebel khususnya set catur. Dengan metode perancangan yang sudah dilakukan antaranya: preliminary design, design development dan final design. Sehingga dihasilkan desain set catur untuk anak-anak yang memiliki gaya desain modern minimalis.

Rapid chess provides an unparalleled laboratory to understand decision making in a natural environment. In a chess game, players choose consecutively around 40 moves in a finite time budget. The goodness of each choice can be determined quantitatively since current chess algorithms estimate precisely the value of a position. Web-based chess produces vast amounts of data, millions of decisions per day, incommensurable with traditional psychological experiments. We generated a database of response times (RTs) and position value in rapid chess games. We measured robust emergent statistical observables: (1) RT distributions are long-tailed and show qualitatively distinct forms at different stages of the game, (2) RT of successive moves are highly correlated both for intra-and inter-player moves. These findings have theoretical implications since they deny two basic assumptions of sequential decision making algorithms: RTs are not stationary and can not be generated by a state-function. Our results also have practical implications. First, we characterized the capacity of blunders and score fluctuations to predict a player strength, which is yet an open problem in chess softwares. Second, we show that the winning likelihood can be reliably estimated from a weighted combination of remaining times and position evaluation.

In this project, we will be designing and implementing a classic version of Chess game with a Graphical User Interface. Each pieces on the Chess game follows the basic rules of Chess including capturing, check and checkmate, and draw; this involves realization of pieces, encoding them using Object-oriented Programming into machine and devising algorithm for their movements. Initially a console based working design will be produced using C++. The code will be later ported into Unity Engine for the graphical implementation. The game is played by two players, either human against human or computer against human. Although we are unable to implement an advanced artificial intelligence for the Chess game due to scope of the project, we will research the algorithms and consider them if they are feasible to be implemented.

We present constant-time testing algorithms for the generalized shogi (Japanese chess) and chess. These problems are known to be EXPTIME-complete. A testing algorithm (or a tester) for a property accepts an input if it has the property and rejects it if it&#39;s far from having the property in high probability (e.g., at least 2/3) by reading only a constant part of the input. A property is said to be testable if there is a tester. The generalized shogi (and chess) problem is, given any position on √n x √n board with O(n) pieces, for testing the property “the player who moves first has a winning strategy.” We present that this property is testable for both shogi and chess. The shogi tester is one-sidederror and the chess tester is surprisingly no-error! In the last decade, many problems have been found to be testable. However, almost all of such problems are in class NP. This is the first result on constant-time testability of EXPTIME-complete problems.