Checkmate Chariot

Table of Contents

1. Introduction
2. Getting Started
   • Prerequisites
   • Installation
3. Roadmap
4. License

Introduction

This project is a unique implementation of a chess engine, developed by Jakub Lisowski, Łukasz Kryczka, and Jakub Pietrzak from the Warsaw University of Technology. It serves as our primary focus and source of enjoyment in programming, providing a platform for exploring various intriguing programming schemes and algorithms. Our goal is to create the most powerful algorithm we can while ensuring well-tested software. While we strive to incorporate our own ideas and solutions, we occasionally draw inspiration from state-of-the-art concepts. This README will guide you through the key aspects of our engine, including its unique features and functionalities.

Getting Started

Prerequisites

• OpenMp - is used to parallelize map parameter search
• GCC compiler
• x86_64 platform
• linux environment - to correctly run tests

Installation

Clone the repository to the desired directory:

   git clone https://github.com/Jlisowskyy/Checkmate-Chariot ; cd Checkmate-Chariot

Use a command below to build the project:

   cmake CMakeLists.txt -DCMAKE_BUILD_TYPE=Release ; make -j {Number of cores on your platform} 

Features

Board Representation

• Bitboards: Utilizes bitboard structures for efficient board state representation.
• Supports FEN Strings: Loads board positions from FEN strings for flexible game setup.
• Move Generation: Implements comprehensive move generation including all standard chess moves, castling, en passant, and promotions.
• Board State Management: Maintains current board state with functionalities to make and unmake moves.

Board Evaluation - a feature that is currently worked on the most

• Material Count: Considers the material balance of pieces.
• Special Pawn Evaluation: Includes specific evaluations for passed, isolated, and doubled pawns.
• King Safety: Evaluates attacked squares around kings.
• Separate Middlegame and Endgame Tables: Uses distinct evaluation metrics for different game phases.

Opening Book

• Predefined Openings: Contains a rich database of opening moves from professional games.
• Move Selection: Selects optimal opening moves to transition into a favorable middle game.
• Learning Capabilities: Can update the opening book based on game outcomes to improve future performance.
• The engine starts with a random move from the opening book

Best Move Search

• Negamax Algorithm: Implements the negamax variant of the minimax algorithm for simplicity.
• Alpha-Beta Pruning: Uses alpha-beta pruning to reduce the number of nodes evaluated in the search tree.
• Iterative Deepening: Implements iterative deepening to progressively deepen the search depth.
• Transposition Tables: Utilizes transposition tables to store and retrieve previously evaluated positions.
• Quiescence Search: Extends search in tactical positions to avoid horizon effect.
• Advanced Pruning Techniques: Includes null move pruning, reverse futility pruning, and delta pruning.
• Late Move Reductions: Reduces the search depth for late moves to improve efficiency.
• Principal Variation Search: Focuses on the most promising moves to find the best line of play.

Move Sorting and Evaluation

• Move Ordering: Sorts moves to prioritize captures, checks, and promotions.
• MVV-LVA: Uses Most Valuable Victim - Least Valuable Attacker heuristic for move ordering.
• Killer Moves: Remembers two killer moves per depth to enhance search efficiency.
• History Heuristic: Sorts moves based on their historical effectiveness.

Time Management

• Dynamic Time Allocation: Adjusts time usage based on the number of moves played and remaining time.
• This is currently work-in-progress

UCI Interface

• Supported Commands: Full UCI implementation
• Hash Size Configuration: Allows setting the hash size via the "Hash" UCI option, with a default of 256 MB.

License

Distributed under the MIT License. See LICENSE.txt for more information.