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A proof-of-concept implementation of the Dots And Boxes game, employing the Minimax Algorithm and different AI heuristics for CPU player.

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riccardoangius/minimax-ai-playground

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The Dots and Boxes game with a sprinkle of AI with Minimax variants and different heuristics, developed by Riccardo Angius. [email protected]

This app was developed and tested on OS X 10.9, Python 3.7. Requiresmodules NetworkX 2 and WxPython 4.

Riccardo Angius [email protected]

Features:

  1. Multiplayer game
  2. Naive Minimax Agent
  3. Pruning, cutoff and ply evaluatio
  4. Transposition table
  5. Successor soting
  6. Backtracking search
  7. Iterative deepening search
  8. Real-time
  9. Quiescence search

Running:

python controller.py

Usage: python controller.py [-h] [-l SIDELENGTH] [-p CPUPLAYERS] [-s SECONDSTODECIDE] [-ca PLAYERCONFIGA] [-cb PLAYERCONFIGB] [-t] [-d] [-q]

optional arguments:

-h, --help  show help message and exit

-l SIDELENGTH, --side_length SIDELENGTH

Number of dots on each side of the gameboard.

-p CPUPLAYERS, --cpu_players CPUPLAYERS

Number of CPU players (min 0, max 2)

-s SECONDSTODECIDE, --seconds_to_decide SECONDSTODECIDE

Seconds allowed to each CPU player to make their

decision.

-ca PLAYERCONFIGA, --player_config_a PLAYERCONFIGA

Configuration for Player A (first to move).

-cb PLAYERCONFIGB, --player_config_b PLAYERCONFIGB

Configuration for Player B (second to move).

-t, --talking_players

Make CPU players print information about their

computations.

-d, --details_on_gameover

Print out experiment details on gameover.

-q, --quit_on_gameover

Exit application on gameover.

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A proof-of-concept implementation of the Dots And Boxes game, employing the Minimax Algorithm and different AI heuristics for CPU player.

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