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AI Strategies in Draughts and Checkers

Draughts, Checkers, Artificial Intelligence, Two Player Games, MiniMax, Alpha beta Pruning, Ant Colony Optimization, Genetic Algorithms
Summary of top
Research on artificial intelligence in draughts (checkers) has explored various techniques to develop strong game-playing programs. Minimax-based algorithms with alpha-beta pruning have been implemented to efficiently evaluate game trees and select high-utility moves (Allsop, 2013; Suyitno, 2005; Jofanda & Yasin, 2021). Genetic algorithms have been employed to optimize board evaluation functions, demonstrating the ability to improve gameplay without external guidance (Allsop, 2013; Chisholm & Bradbeer, 1997). These AI-powered checkers games have been developed for computers and smartphones, allowing players to enjoy the game anytime (Jofanda & Yasin, 2021). Testing of an AI-based checkers game showed varying win rates for human players across different difficulty levels, with 60% at easy, 40% at medium, and 20% at hard levels (Jofanda & Yasin, 2021). The application of AI techniques in draughts not only enhances gameplay but also serves as a platform for studying and explaining artificial intelligence in games (Allsop, 2013; Jofanda & Yasin, 2021).
Paper
Abstract summary
Artificial Intelligence Techniques AppliedTo Draughts
D. Allsop
2013 ·
0 citations
The paper develops a draughts-playing program that learns game strategies using AI techniques like minimax, alpha-beta pruning, and genetic algorithms.
Pembuatan Perangkat Lunak Untuk Permainan Draught (Checkers) Dengan Menggunakan Alogaritma Alpha-Beta Pruning
Rini Widayanti Suyitno
2005 ·
0 citations
The paper describes the development of a checkers game software using the Alpha-Beta Pruning algorithm.
Design of Checkers Game Using Alpha-Beta Pruning Algorithm
Achmad Naufal Wijaya Jofanda +1
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi
2021 ·
2 citations
PDFDOI
The paper describes the design of a checkers game using the alpha-beta pruning algorithm, an optimization technique from the minimax algorithm.
Machine learning using a genetic algorithm to optimise a draughts program board evaluation function
Kenneth J. Chisholm +1
International Conference on Evolutionary Computation
1997 ·
9 citations
DOI
The paper uses a genetic algorithm to optimize the board evaluation function of a draughts (checkers) program.
Implementation of Alpha-Beta Pruning and Transposition Tables on Checkers Game
Cristian C. Suancha +2
IEEE Access
2024 ·
0 citations
DOI
The paper implements alpha-beta pruning and transposition tables to optimize the AI in a web-based checkers game.
LS-Draughts - A Draughts Learning System based on genetic algorithms, neural network and temporal differences
H. C. Neto +1
IEEE Congress on Evolutionary Computation
2007 ·
16 citations
DOI
LS-Draughts is a learning system for draughts (checkers) that uses genetic algorithms, neural networks, and temporal differences to automatically generate an effective set of features and train a player agent.
An Application of Genetic Algorithm to the Game of Checkers
Gabriella A. B. Barros +3
Brazilian Symposium on Games and Digital Entertainment
2011 ·
3 citations
DOI
Genetic algorithms can be used to create an effective AI opponent for the game of checkers.
Analysis of Game Tree Search Algorithms Using Minimax Algorithm and Alpha-Beta Pruning
Prof. Sumit S Shevtekar +2
International Journal of Scientific Research in Computer Science Engineering and Information Technology
2022 ·
1 citation
PDFDOI
The paper analyzes game tree search algorithms using the Minimax algorithm and Alpha-Beta pruning for two-player games.