Connect 4 :

> Write the evaluation function in English.

> Implement the evaluation function. Write the code in JAVA.

>Have your intelligent agent play against a random agent for 100 games and record the results.

> The program will play the game automatically. One player, the intelligent agent, will be using the MiniMax algorithm with your evaluation function. It will only evaluate the current move. It will not look ahead to future moves. (We could argue that this makes is a Max algorithm, not MiniMax, but this is semantics).

> The other player will select moves totally at random, but it will only select legal moves.

> To show that your evaluation function is good, you will show that your agent never loses. To show this, have your program play 100 times and show that every game shows the intelligent agent as either winning or as a draw.

> Your final version of the program that you will submit will include the final board for 10 games and the number of wins and draws for all 100 games.

> Submit your code and a screen shot of the output from your program that shows the number of wins and draws.

> Implement a feature to look one move ahead to create a true MiniMax algorithm

> Implement alpha-beta pruning.

Do the following:

1) Apply MiniMax and explore future moves for more plies

2) Apply Alpha-Beta Pruning.

3) Have your intelligent agent play against the intelligent agent from another student in the class and report the results.

If you can automate this and have them play for 100 games.

CODE IN JAVA