Using the sample code given, Implement plain minimax, alpha-beta pruning and depth-limited version of minimax

This is a connect 4 game. The task in this programming assignment is to implement an agent that plays the Max-Connect4 game using search. The sample code for the game is:
Java: download files here. Compile using:javac maxconnect4.java GameBoard.java AiPlayer.javaAn example command line that runs the program (assuming that you have input1.txt saved in the same directory) is:java maxconnect4 one-move input1.txt output1.txt 10
C : download file here. Compile using:g -o maxconnect4 maxconnect.cppAn example command line that runs the program (assuming that you have input1.txt saved in the same directory) is:maxconnect4 one-move input1.txt output1.txt 10
Python (Version 2.4): download file here.An example command line that runs the program (assuming that you have input1.txt saved in the same directory) is:./maxconnect4.py one-move input1.txt output1.txt 10
Implement plain minimax.
Implement alpha-beta pruning (if correctly implemented, you also get the points for plain minimax, you don’t need to have separate implementations for it).
Implementing the depth-limited version of minimax (if correctly implemented, and includes alpha-beta pruning, you also get the points for plain minimax and points for alpha-beta search, you don’t need to have separate implementations for those). For full credit, you obviously need to come up with a reasonable evaluation function to be used in the context of depth-limited search. A “reasonable” evaluation function is defined to be an evaluation function that allows your program to consistently beat a random player.
Include in your submission an table of CPU runtime (for making a single move) vs depth, when the board is empty (input1.txt). Your table should include every single depth, until (and including) the first depth for which the time exceeds one minute.

 

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