Pacman and Ghost Agent | Python | Artificial Intelligence | Search-based Algorithms | Learning-based Algorithms
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Updated
Feb 18, 2017 - Python
Pacman and Ghost Agent | Python | Artificial Intelligence | Search-based Algorithms | Learning-based Algorithms
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
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"
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.
Mini-max, Alpha-Beta pruning, Expectimax techniques are used to implement multi-agent pacman adversarial search.
Adverserial agent for isolation game
Simplified version of Chess to recommend next move for the input board using Minimax Game Search Algorithm with Alpha-Beta Pruning
Solve 2048 using minimax and alpha beta pruning
EECS 348: Intro to Artificial Intelligence, Assignment 2 (Python)
Adversarial search agent to play the game "Isolation": minimax search, minimax + alpha-beta pruning + iterative deepening
Halma game with an AI player, move validation, and dynamic board sizing
AI projects on: minimax algorithm, variations of nqueens problem, and policy iteration in Markov Decision Processes
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