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Registered Member #1062
Joined: Tue Oct 16 2007, 02:01AM
Location:
Posts: 1529
I recently got interested in "learning" programs, so im going to try to make my own learning program in Java for tic tac toe. This is the method i have.
The normal tic tac toe program, but it records all the moves you make, and it makes. At the end of the game if it (computer); Looses: Subtracts 1 "point" from the positions it moved in an array Adds 1 "point" to whatever the player did Wins: Adds 1 point to what it moved Subtract 1 point to what the player moved
The points will have a maximum, say 100, and start at the midpoint [50]. Minimum will be zero. Im thinking the more games it plays, itle "realize" the best place to move, by increasing the probability of the program to choose the position on the board.
I know this wont work for different player very well, with different playing styles and all, and the tic tac toe tricks some people use.
Registered Member #27
Joined: Fri Feb 03 2006, 02:20AM
Location: Hyperborea
Posts: 2058
Is this on a 3x3 grid?
If I understand your explanation correctly you will give each position in the grid a value. So the computer will play the position with the highest value that is still free.
The problem is that the value of a position depends on the previous moves in the game and not on the average of all games. Since the computer will not use the current state of the grid to assign a value to each position it will not be able to block the opponent and the opponent will have a very easy game.
This strategy will work in Reversi to a small degree since different positions have a very different value (a corner can't be reversed so it is very valuable). In this case I am not sure it will do much good.
Registered Member #1062
Joined: Tue Oct 16 2007, 02:01AM
Location:
Posts: 1529
yes, its a 3x3 grid. Im getting a book on artificial neural networks in java, so ile wait for that. I was thinking to have it think to check for wins that the player might have in the future(eg. 2 X in row). If it doesn't then it applies the stratagy.
Registered Member #27
Joined: Fri Feb 03 2006, 02:20AM
Location: Hyperborea
Posts: 2058
Let us think about it a little, there are 9 squares with 3 possible states each, so the total number of states of the board is 3^9 = 19 683.
There are at most 9 possible places to move, that fits in 4 bits. So a table of 9842 bytes can store every possible state of the board and the best move for each state. If we account for different symmetries then the table becomes a lot smaller.
Then there is a matter of finding out what is the optimal move for a given state. You could use your first method only now you have an array for each possible state and let it learn as it plays, or you can just play all possible games that can result from each possible state once and for all.
When the game starts there will be 9 free positions, one the next move there will be 8 and so on. So there should be 9*8*...*1 = 9! = 362 880 different games to play. So filling the table using brute force should take at most (362 880 * 19 683) = 7 142 567 040 games.
The real number will be considerably smaller since the games does not run from the start and many of them will end before each position on the board is used. Some of the 19 683 states also describe games that have already ended. It should not take long to run through all possible combinations.
Geometrically Frustrated Registered Member #6
Joined: Thu Feb 02 2006, 04:18AM
Location: Bowdoin, Maine
Posts: 373
I don't know a whole lot about computer algorithms, but the first thing that came to my mind was the Markov chain. Perhaps you could use a high order Markov chain where only 'winning' moves are stored. That might end up being a large amount of data, and you'd need to give it some way to decide what to do in situations it hasn't seen before, so it's probably not the best system. Something to think about, anyway.
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"Learning" Tic Taco Toe
