Mu_Torere game with AI
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Updated
May 14, 2016 - Ruby
Mu_Torere game with AI
An advanced AI to play the 2-player board game Othello
Tests Minimax, Alpha-beta strategies in a (Comp vs. Comp) board game.
Pacman and Ghost Agent | Python | Artificial Intelligence | Search-based Algorithms | Learning-based Algorithms
Java Chess Game with AI Computer player
Player vs CPU Tic-Tac-Toe game using the Minimax Algorithm with Alpha-Beta Pruning.
Term 1 Project 1 Build a Game-Playing Agent by Luke Schoen for Udacity Artificial Intelligence Nanodegree (AIND)
Implementation of reinforcement learning algorithms to solve pacman game. Part of CS188 AI course from UC Berkeley.
Project: Build a Game-playing Agent | Artificial Intelligence Nanodegree | Udacity
Game of isolation done as part of the Nanodegree program from Udacity
Game-playing Agent. Implementing Minimax Search and AlphaBeta Pruning techinques for solving
project submitted at AIND (May cohort)
Game playing agent using modified alpha-beta search
Implementing Minimax search
Udacity AI Nanodegree's Project for a Game playing agent for Isolation. This project uses algorithms like minimax search, alpha beta pruning and iterative deepening to create a game playing agent for a zero sum board game like Isolation.
an adversarial search agent to play the game "Isolation"
🎮 使用α-β剪枝(Alpha-beta-pruning)的极小极大算法(Minimax-algorithm)实现的井字棋(一字棋、tic-tac-toe)游戏。
In this project, agents are designed for the classic version of Pacman, including ghosts. Mini-max, Alpha-Beta pruning, Expectimax techniques were used to implement multi-agent pacman adversarial search.
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