Automated Chess Tutor

Applied Intelligence, 1993

Most research in computer chess has focused on creating an excellent chess player, with relatively little concern given to modeling how humans play chess. The research reported in this article is aimed at investigating knowledge-based chess in the context of building a prototype chess tutor, UMRAO, which helps students learn how to play bishop-pawn endgames. In tutoring it is essential to take a knowledge-based approach, since students must learn how to manipulate strategic concepts, not how to carry out large-scale lookahead searches.

Proceedings of the 5th International Conference on Computer Supported Education, 2013

We present the development of an intelligent tutoring system for chess endgames, and explain in detail the system’s architecture that is comprised of five essential components. The rule-based domain model contains a conceptualized domain theory, which serves as a bridge between the basic declarative domain theory andprocedural knowledge for concrete problem solving. The search engine is used to generate new chess problems and to validate students’ solutions on the fly. The tutoring model represents pedagogical knowledge and follows the standard model-tracing approach. The student model contains the knowledge about the user in the form of a skill meter, aiming to show the level of a student’s understanding of particular skills. Finally, the user interface is where the interaction between a student and the tutor takes place.

Computers & Education, 1996

Abetract--This paper deals with the problem of teaching chess. We describe the ideal ~stics for a Insming environment for chess and discuss a psrlinl impismentmion of such an environment; ICONCitESS, An Inm~ve Consultant for Chess Middiepmes. Most research on computer chess has focused on c~eatinll highly competitive chess playing pmlpune, rngmelms of the means used to w.hleve this goal. Because of the s,___,~___ obtained by programs based on search algorithms, little effort has been put on chess playing proMrams that play using high level mrmegics, which me necessary in human chess players. This lack of sumegic foundations maims most chess playing pro~mns inadequate as chess tutors too. This paper presents a new approach for computer chess in ~ and specifically for a lenming environment for chess. This approach combines the wclmiqnes of fuzzy logic and casebnsed renmning in order to produce hish level edvice for cbess pnsitions. We also lnsent the resulm of the mnpirkal experiments used to test ICONCEIESS and suggest some ways in which the aplronch can be applied to other domains.

1977

This thesis describes an investigation of the problems involved in representing knowledge within the task area of elementary Chess endgames. Two major criteria are taken for the choice of a model of & the chessplayer's knowledge : firstly, that algorithms constructed using the model should be natural from the viewpoint of a chessplayer and commensurate with his, view of the complexity of the task, and secondly that the algorithms should be capable of refinement in the light of experience in a manner which preserves the previous property. Elementary chess endgames are studied as a field in which programs based on tree-searching and traditional evaluation functions have achieved poor results and where tree-searching seems to play little or no part for people. It is therefore possible to examine problems of knowledge representation and program refinement largely independently of the tree-searching paradigm. A long term aim of the research is to develop a representation suitable as ...

Lecture Notes in Computer Science, 1995

Current computer-chess programs achieve outstanding results in chess playing. However, there is a deficiency of evaluative comments on chess positions. In this paper, we propose a case-based model that supplies a comprehensive positional analysis for any given position. This analysis contains evaluative comments for the most significant basic features found in the position and a general evaluation for the entire position. The analysis of the entire position is presented by an appropriate Multiple eXplanation Pattern (MXP), while the analysis of each chosen feature is presented by a suitable eXplanation Pattern (XP). The proposed analysis can improve weak and intermediate players' play in general and their understanding, evaluating and planning abilities in particular. This model is part of an intelligent educational chess system which is under development. At present, our model deals only with a static evaluation of chess positions; addition of searching and playing modules remains for future work.

Lecture Notes in Computer Science, 1995

In this paper we describe game-independent strategies, capable of learning explanation patterns (XPs) for evaluation of any basic game pattern. A basic game pattern is defined as a minimal configuration of a small number of pieces and squares which describes only one salient game feature. Each basic pattern can be evaluated by a suitable XP. We have developed five gameindependent strategies (replacement, specialization, generalization, deletion, and insertion) capable of learning XPs or parts of them. Learned XPs can direct players' attention to important analysis that might have been overlooked otherwise. These XPs can improve their understanding, evaluating and planning abilities. At present, the application is only in the domain of chess. The proposed strategies have been further developed into 21 specific chess strategies, which are incorporated in an intelligent educational chess system that is under development.

With increases in computational power and better algorithms, chess engines are able to explore game trees of greater depths and thus have excelled at calculative play. However, since the growth of the move search space is exponential, even with increase in computational power chess engines perform only marginally better year-toyear. In this paper, we explore a novel technique for assessing board positions using machine learning techniques that can be used to supplement and improve current chess engines. We model chess board positions as networks of interacting pieces and use supervised machine learning techniques to analyze positions and predict outcomes in chess games.

2023

This paper reviews the many cognitive processes involved with the game of chess. These processes vary from novice players to grandmasters. Chess is often used as a marker for intelligence in humans and thus it is a baseline for AI research and the use of ML models (Ensmenger, 2011). This paper focuses on two key aspects of chess: the cognitive processes involved in gaining chess expertise and the use of computers in the chess world. Analysis of cognitive processes in chess includes topics such as pattern recognition, chunking in memory, and problem-solving. The paper will further analyze how machines learn to play chess and the differences between machine and human players. With proper understanding of chess and the cognitive processes involved with playing, chess can be used as a tool to facilitate the gaining of expertise in other domains.

berkantakin.com

This paper describes a computer program, which is able to play chess. The program performs three main tasks as in all chess-playing computer programs; board representation, a search algorithm, and an evaluation function. Board representation shows the placement of the pieces on a graphical user interface (GUI), and handles the moves of the pieces to comply with the rules of chess. The search algorithm runs minimax algorithm based on α α α α-β β β β pruning with move ordering heuristics before selecting the next move, and the next move is decided according to the result of the evaluation function.

Lecture Notes in Computer Science, 2010

Complete tablebases, indicating best moves for every position, exist for chess endgames. There is no doubt that tablebases contain a wealth of knowledge, however, mining for this knowledge, manually or automatically, proved as extremely difficult. Recently, we developed an approach that combines specialized minimax search with argumentbased machine learning (ABML) paradigm. In this paper, we put this approach to test in an attempt to elicit human-understandable knowledge from tablebases. Specifically, we semi-automatically synthesize knowledge from the KBNK tablebase for teaching the difficult king, bishop, and knight versus the lone king endgame.