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An AI bot for playing the two-player board of Yinsh having an average branching factor of 35. Uses Min-Max with alpha-Beta Pruning to find the best possible move using a custom made evalution for each board position.

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Yinsh

Simulator supporting smart agents and a user interface for Yinsh, an abstract strategy board game.

Rules

The rules of the game can be found here

Dependencies

  • Selenium
  • Jinja2
  • Chrome Webdriver

Main Files

  • game.py - This has an instance of the game. It can be run locally to hand play a game of Yinish or re-play a recorded game. Should be run in GUI mode to make game board visible.
  • RandomPlayer.py - This is an implementation of a random bot. It is a good place to start understanding the flow of the game and the various game states.
  • client.py - This will encapsulate your process and help it connect to the game server.

    ip (mandatory) - The Server IP.
    port (mandatory) - The Server Port.
    exe (mandatory) - The Executable.
    mode (optional) - The View Mode ('GUI' / 'CUI'). Default: 'CUI'

  • server.py - This connects the clients and manages the transfer of information.

    port (mandatory) - The Server Port.
    ip (optional) - The Server IP. Default: 0.0.0.0
    n (optional) - The Board Size. Default: 5
    NC (optional) - Number of Clients. Default: 2
    TL (optional) - Time Limit. Default:150
    LOG (optional) - The Log File.

Run Instructions

Here are the sample instructions used to match two random players against each other over the server network.

Setup Server

python server.py 10000 -n 5 -NC 2 -TL 150 -LOG server.log

Setup Client 1

python client.py 0.0.0.0 10000 RandomPlayer.py -mode GUI

Setup Client 2

python client.py 0.0.0.0 10000 RandomPlayer.py

Gameplay

The game play consists of the players executing a sequence of moves in a single turn. A move is a triple: movetype hexagon position.

Movetype

  • P - Place a ring
  • S - Select a ring
  • M - Move a ring
  • RS - Remove a row Start
  • RE - Remove a row End
  • X - Remove a ring

Hexagon

The board is divided into hexagons. The center point is referenced as hexagon 0. It is surrounded by hexagon 1, then 2 and so on.

Position

For a selected hexagon, the position refers to a particular point on the hexagon. Hexagon h will have 6*h positions referenced from 0 to 6*h-1. The topmost point is point 0 with increasing postions following in a clockwise direction.

Examples

Place a Ring

To place a ring on hexagon 1 and position 2 we will play the move P 1 2

Move a Ring

To move a ring from hexagon 1 and position 2 to hexagon 2 postion 4 we will play the move sequence S 1 2 M 2 4

Remove a Row and Ring

To remove a row we have to specify the start of the row using RS and the end of the row using RE RS 1 2 RE 4 16.
This is followed by removing any ring X 3 4.
In general a Remove Row will be triggered by a Move Ring move sequence. Hence the overall move sequence will look like S 1 2 M 2 4 RS 1 2 RE 4 16 X 3 4.

Scoring

At the end of a game both players will be given a score. The game score consists of two parts:

  1. The Ring Margin
  2. The Marker Margin

The Ring Margin

This score will be based on the extent of victory. It is calculated as follows:

Your Rings Removed Opponents Rings Removed Ring Margin Score
3 0 10
3 1 9
3 2 8
2 0 7
2 1 6
1 0 6
2 2 5
1 1 5
0 0 5
0 1 4
1 2 4
0 2 3
2 3 2
1 3 1
0 3 0

The Marker Margin

This score directly depends on the number of markers you have left at the end of the game. It is calculated as follows:
Marker Margin Score = # Markers Remaining / 1000

Final Score

The final score is simply: Ring Margin Score.Marker Margin Score Example. Assume the following:
Player 1 has removed 3 rings and has 12 markers left on the board.
Player 2 has removed 1 ring and has 17 markers left on the board.
Player 1 score will be: 9.012
Player 2 score will be: 1.017

Note) Incase a player suffers a TIMEOUT, he will automatically lose the gane and it will count as a (0-3) defeat towards the player and a (3-0) win for the opponent.

About

An AI bot for playing the two-player board of Yinsh having an average branching factor of 35. Uses Min-Max with alpha-Beta Pruning to find the best possible move using a custom made evalution for each board position.

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