Medina

An attempt to utilize machine learning to play the board game Medina

Libraries used for the project

  • Tkinter - for GUI elements Tkinter is usually installed with most distribution of python, to check if it is installed, open up the python3 interpreter and try ~~~~ import tkinter tkinter._test() ~~~~

This should give a basic window that can be interacted with.

if it does not give a window, the command to install tkinter on Ubuntu is 'sudo apt-get install python3-tk', more specific information for each os to install tkinter can be found here

Tkinter will be used to make the GUI for the game.

  • NumPy - mathematics library

to install NumPy, use pip:

  pip3 install numpy

or the equivalent

  python3 -m pip install numpy

NumPy will be useful to compute and do operations on the large amounts of numbers and math involved in analyzing a board game.

  • Tensor Flow - Neural Network library

    to install tensorflow, use pip:

    pip3 install tensorflow
    

    or the equivalent

    python3 -m pip install tensorflow
    

Tensorflow is used to make and read neural networks.

The Game

Media is a board game published by Stronghold Games ( more information here, official website for game ) and designed by Stefan Dorra. I claim to have no ownership of the game and this project is only an attempt to use machine learning to play the game.

The game is played by two to four players and the players all build a city together. While building the city, players can claim buildings for points. Buildings near the well, market or walls are worth more points than buildings not close to anything important in the city.

A game being played

Game in progress

Image from Board Game Geek by Julian Pombo uploaded on 2015-08-03 image source

Each player is given a limited amount of resources and on each of their turns, players can build the city or claim a building; players can take a total of two actions on each of their turns. There are four different colors of buildings and each player can only claim one of each color. After every player has claimed a building of each color or no more actions can be taken, the game ends.

A player can place any two or two of the same pieces from the following list on each of their turns:

  1. Buildings : can be used to start a new building or grow an existing, unclaimed building. Four colors of buildings: Grey, Brown, Orange and Purple.
  2. Rooftops : can be used to claim an unclaimed building currently on the board. A player can only own one of each color building.
  3. Stables : can be attached to an existing claimed building and grows the building for purposes of ardency and scoring.
  4. Merchant : merchants build in a claim across the board and award extra points to buildings that the merchants are next to.
  5. Walls : walls are built around the edge of the board growing out from towers at the corners. Walls that are adjacent to buildings award extra points to the building for each wall touching the building.

While building, there are a few restrictions that players can utilize to take advantage of the current board and further their own score or hurt other players ability to play. For example, only one unclaimed building of each color can be built at a time and once a building is claimed it can only be extended by attaching stables.

Once the game has ended, the buildings each player has claimed scores based on the position and elements around the building (walls, the well, merchants and stables all give additional points). For the full rules and scoring of the game, view this pdf

Objective

My objective is this project is to:

  • Implement the game in Python with a GUI interface and allow players to play the game.
  • Make the game a network game so multiple players could play the same game on different machines. Network game play is a lower objective
  • Add an AI to the game that utilizes machine learning and pattern recognition to make moves and become better at playing the game as time goes on.
  • Give the AI the ability to watch and learn from records of games.
  • Train the AI to the point in which it can consistently play the game and get a decent score.
  • Possibly develop different versions of the AI that can play the game with different strategies (aggressive, risky, impatient) and difficulty.

Purpose

This project is the project for Python Programming course at The University of Cincinnati Fall Semester 2016.

Copyright

This code is under the MIT License Copyright (c) 2016 Nicholas Maltbie, Jeet Shah, Aaron Assaf. See LICENSE.txt for further details.

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