Place both th files in same directory and execute handler.py, like this python3 handler.Categories: (5), 'Distributed Computing' (1), - (1). Cool part is your agent can also be in Java and handler takes care. The handler.py plays the game between agents created by halma.py. The game starts with each player's camp filled by pieces of their own color.Each player has a set of pieces in a distinct color, of the same number as squares in each.For two-player games, each player's camp is a cluster of 19 squares.Each player's camp consists of a cluster of adjacent squares in one corner of the board.The board consists of a grid of 16×16 squares.Simple wooden pawn-style playing pieces, often called 'Halma pawns.'.This agent solves the 2 player variant of Halma.

See for more about this rule modification. Here, to prevent spoiling, the goal of the game is to occupy all of the opponent’s starting positions which the opponent is not still occupying. Note that the spoiling player cannot win either (because some pieces remain in their original corner and thus cannot be used to occupy all positions in the opposite corner).

Note that this original rule of the game is subject to spoiling, as a player may choose to not move some pieces at all, thereby preventing the opponent from occupying those locations.

To win the game, a player needs to transfer all of their pieces from their starting corner to the opposite corner, into the positions that were initially occupied by the opponent. Each player starts with 19 game pieces clustered in diagonally opposite corners of the board. The game uses a 16x16 checkered gameboard. In this project, we will play the game of Halma, an adversarial game with some similarities to checkers.

Halma game ai agent java code github