Othello Game Application Implemented with Alpha-Beta Algorithm in SWI-Prolog's Logic Programming Language ♟️
A small Strategy Game that is developed with the basic knowledge of Flutter. Live on Play Store with 10K+ downloads along with 2K+ active installs.
The ESP32 Cheap Yellow Device is a fantastic device as it comes with a 2.8-inches TFT display with Touch Controller and SD Card Reader mounted on the PCB. This is the ideal platform to run a touch based Othello / Reversi game.
A DOS-based Othello (Реверси) game, written using Borland C++ graphics driver. Supports PvP and PvC modes.
Othello is a strategy board game created in 1971 based on Reversi game. This game is an ai-ai battle only (alphabeta or negamax algorithm)
The Moor of Venice is a strategy game inspired by Shakespeare's Othello. Immerse yourself in a historical setting, outwit opponents, and master the art of tactical gameplay.
A reversi game with AI.
ゲームができるサイトです。学校で使えるようにしています
Othello/Reversi Game in Python3, equipped with GUI + AI Player, made using pygame.
othello bot implemented in Rust.
CELLS is an infinite board Reversi game. ：CELLS は、無限盤面リバーシゲームです。従来のリバーシゲームの概念を覆し、「角」も「端」も存在しない無限盤面で、あなたの戦略が試されます。
An Othello game with visualization via command line and with a game bot using alpha–beta pruning algorithm over minimax.
Implementation of the classic board game Othello (also known as Reversi). It features a full graphical user interface (GUI) built with Pygame and an intelligent AI opponent powered by the Alpha-Beta Pruning algorithm.
An Othello bot made with LEGO Mindstorms.
A strategy game between the user and the Computer. Simple implication of AI
Machine Learning
I implemented this Othello or reversi AI (Artificial Intelligence) game in C++ language. This is my AI lab project. Here, I implemented its Human vs Computer player. [8x8 Board size]
MCTS and Minimax Alpha-beta pruning for Othello/Reversi in Python with benchmark
This project is a C++ implementation of the Othello board game, featuring an AI opponent powered by the Minimax algorithm with Alpha-Beta Pruning for optimized decision-making. It incorporates a custom heuristic evaluation function based on strategic board positioning and piece count, supporting variable search depths for adjustable difficulty.
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