Adversarial Constraint Satisfaction by Game-tree Search Python version 3.5

The map coloring game obeys the following rules:

1. There are two players, Player 1 and the opponent Player 2. Each player takes turns as in chess or tic-tac-toe. That is, Player 1 takes a move to color a node, then Player 2, then back to Player 1 and so forth. In the game, you could assume Player 1 will always start first (i.e. Player 1 is the MAX player, Player 2 is the MIN player).
2. The players can only color neighbors of nodes that have already been colored in the map. Adjacent nodes could not have the same color.
3. The score of each player is defined as the total sum of weights of their colored nodes.
4. The terminal state of the game is that no more nodes could be colored in the map based on rule 2. It could be either all nodes in the map have been colored, or no possible assignment could be made according to rule 2. 1 Brown, K. N., Little, J., Creed, P. J., & Freuder, E. C. (2004). Adversarial Constraint Satisfaction by Game-Tree Search. In ECAI (pp. 151–155). Retrieved from https://www.frontiersinai.com/ecai/ecai2004/ecai04/pdf/p0151.pdf
5. The evaluation function of the colored map state could be defined as Score_Player1 – Score_Player2. The leaf node values are always calculated from this evaluation function. Although there might be a better evaluation function, you should comply with this rule for simplicity. Node Expanding rule in alpha-beta pruning algorithm: When expanding a node in the game tree (i.e. choosing an action), you need to expand first based on the alphabetical order of the state name, and then based on the color name (if state names are equal). For example, if you have the (state, color) pair (CA,G) and (Q,R) , you need to first expand (CA,G).And If you have the pair (CA,G) and (CA,R), you need expand (CA,G) first.

Pass the input file as a command line argument