Fundamental of AI course which focuses on search, multiagents, mdp and reinforcement learning algorithms.

Implemented a expectiminimax agent (2-ply search) with alpha – beta pruning and forward pruning (to reduce the branching factor in the game tree) to determine the best move give the state of the board.

Artificial Intelligence

Artificial Intelligence and Machine Learning - Team Project - Dice Wars

Kami dari kelas Kecerdasan Buatan D Kelompok 6 akan mengimplementasikan algoritma-algoritma yang telah diajarkan pada Final Project ETS kali ini

This project is a Yinsh board game implemented with JavaFX with two types of bots: Alpha-beta prunning bot and Deep Q Network bot

Implementation of Udacity Nanodegree adversial search project using Monte Carlo Tree Search (MCTS). My implementation is a modification of the MCTS at "https://github.com/int8/monte-carlo-tree-search" to suit the project's knights isolation game. My implementation is in the "my_custom_player.py" file

this repository contains my codes for fundamentals of AI course projects

TicTacToe is a fun game, implemented using Minimax algorithm

Implements an agent to play Othello with adversarial search

This project implements a Pacman agent using the minimax algorithm with alpha-beta pruning and a custom evaluation function.

🤖 Build advanced multi-agent AI systems with this high-performance Rust framework, enabling automation, reasoning, and enterprise-level features.