Principles of AI concepts from the curriculum
Contains 5th Semester AIML Lab programs
Command line chess program written in Ruby
A project for the Artificial Intelligence course, implementing XOShift, a strategic board game with custom AI agents using search algorithms and heuristics.
♜A kicking chess game with minmax and alphabeta pruning but not written by Chuck Norris. Queen's gambit enabled.
A Negamaxing Chess program in Java
A console-based Python game where you battle an AI in the board game Triggle. Built with the Minimax algorithm and alpha-beta pruning for strategic, turn-based gameplay.
Ground-up chess engine written in python using hand crafted evaluation functions and search tree. Currently open source, feel free to clone and make some improvements :)
Connect Four agents from random play through DQN, with measured experiments and a live demo. AlphaZero coming soon.
Tic Tac Toe using Random, Minimax, & Alpha Beta Pruning Agent
A robust Python implementation of the Isolation board game featuring adversarial search agents (Minimax and Alpha-Beta Pruning). Includes iterative deepening, heuristic optimizations, comprehensive performance benchmarking, and automated visualization tools for search trees and game states.
A highly optimized Gomoku (Ninuki-Renju) game engine and AI in C, using Negamax, Zobrist transposition tables, a VCF solver, and the MLX42 graphics library.
TicTacToe: the game
Minimax algorithm is used with Alpha-beta pruning in programming
Othello Player in Java: NegaScout search, iterative deepening, killer-move ordering, time management. Coursework.
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