A program for Progressive chess

Lecture Notes in Computer Science, 2015

We present the design of a computer program for playing Progressive Chess. In this game, rather than just making one move per turn, players play progressively longer series of moves. Our program follows the generally recommended strategy for this game, which consists of three phases: looking for possibilities to checkmate the opponent, playing generally good moves when no checkmate can be found, and preventing checkmates from the opponent. In this paper, we focus on efficiently searching for checkmates, putting to test various heuristics for guiding the search. We also present the findings of self-play experiments between different versions of the program.

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.

1991

Article prepared for the 2nd edition of the ENCYCLOPEDIA OF ARTIFICIAL INTELLIGENCE, S. Shapiro (editor), to be published by John Wiley, 1992. This report is for information and review only.

ICGA Journal

Proceedings of the ACM '81 conference on - ACM 81, 1981

Two papers will be presented and a general discussion period will then follow. The panel members are all members of the ACM Computer Chess Committee. The first paper, which appears elsewhere in the Proceedings, is the work of Tony Marsland. It is entitled “A survey of enhancements to the alpha-beta algorithm.” The paper reviews move ordering and search reduction techniques

Arxiv preprint arXiv: …, 2010

In this paper we introduce a novel method for automatically tuning the search parameters of a chess program using genetic algorithms. Our results show that a large set of parameter values can be learned automatically, such that the resulting performance is comparable with that of manually tuned parameters of top tournament-playing chess programs.

INFOR: Information Systems and Operational Research, 1973

The purpose of this paper is to discuss ideas used in current chess playing programs. A short history of events leading to the present state of the art is given and a survey made of present day programs. The Newell, Shaw, and Simon program of 1958 is included since it embodies useful ideas that other programs appear not to employ. The possible performance limits for current techniques will be considered, including reasons for these beliefs. A summary of the major ideas contained in these programs is then presented and suggestions made for the improvement and development of future chess-playing programs. RESUME Le but de cet article est de discuter certaines iddes utilisdes dans les programmes pour le jeu d'echecs. On pr&ente un bref historique des ^vfenements qui ont abouti a l'dtat actuel. On fait la revue des programmes actuels. Le programme de Newell, Shaw, et Simon, ^crit en 1958, est inclus. En effet, il incorpore certaines id^es utiles qui ne semblent pas Stre encore exploit^es dans les programmes actuels. Les limitations des mdthodes courantes ainsi que leurs causes sont considdr^es. Finalement, on resume les id^es principales contenues dans ces programmes et on pr^sente des suggestions pour l'am^lioration et le d^veloppement des programmes qui jouent aux dchecs.

Lecture Notes in Computer Science, 2013

In this article, we explain a new chess variant that is more challenging for humans than the standard version of the game. A new rule states that either player has the right to switch sides if a 'chain' or link of pieces is created on the board. This appears to increase significantly the complexity of chess, as perceived by players, but not the actual size of its game tree. 'Search' therefore becomes less of an issue. The advantage of this variant is that it allows research into board games to focus on the 'higher level' aspects of intelligence by building upon the approaches used in existing chess engines. We argue that this new variant can therefore more easily contribute to gaming AI than other games of high complexity.

2011

The article describes a model of chess based on information theory. A mathematical model of the partial depth scheme is outlined and a formula for the partial depth added for each ply is calculated from the principles of the model. An implementation of alpha-beta with partial depth is given. The method is tested using an experimental strategy having as objective to show the effect of allocation of a higher amount of search resources on areas of the search tree with higher information. The search proceeds in the direction of lines with higher information gain. The effects on search performance of allocating higher search resources on lines with higher information gain are tested experimentaly and conclusive results are obtained. In order to isolate the effects of the partial depth scheme no other heuristic is used.

Computational Intelligence, 1996

This paper introduces METAGAMER, the first program designed within the paradigm of Metagame-playing (Metagame). This program plays games in the class of symmetric chess-like games, which includes chess, Chinese chess, checkers, draughts, and Shogi. METAGAMER takes as input the rules of a specific game and analyzes those rules to construct an efficient representation and an evaluation function for that game; they are used by a generic search engine. The strategic analysis performed by METAGAMER relates a set of general knowledge sources to the details of the particular game. Among other properties, this analysis determines the relative value of the different pieces in a given game. Although METAGAMER does not learn from experience, the values resulting from its analysis are qualitatively similar to values used by experts on known games and are sufficient to produce competitive performance the first time METAGAMER plays a new game. Besides being the first Metagame-playing program, this is the first program to have derived useful piece values directly from analysis of the rules of different games. This paper describes the knowledge implemented in METAGAMER, illustrates the piece values ME?AGAMER derives for chess and checkers, and discusses experiments with METAGAMER on both existing and newly generated games.