Difference between revisions of "CISC181 S2017 Lab4"
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===Simple tic-tac-toe AI=== | ===Simple tic-tac-toe AI=== | ||
| − | The idea behind our Monte Carlo approach to tic-tac-toe move selection is simple. Some moves may lead to a certain win or defeat, while others make victory more or less likely, but not guaranteed. | + | The idea behind our Monte Carlo approach to tic-tac-toe move selection is simple. Some moves may lead to a certain win or defeat (either immediately or inevitably), while others simply make victory more or less likely, but not guaranteed. Given a choice of ''n'' open spaces on the board, we would like to assess the likelihood of victory or defeat resulting from a '''first move''' in each of the ''n'' spaces. Whichever candidate move has the highest likelihood of victory is the one we make. Then the opponent makes their move and we repeat the process. |
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| + | It is not always possible or easy to calculate such a win likelihood exactly, so we try to estimate the likelihood with a probabilistic method. | ||
For more background, see [https://en.wikipedia.org/wiki/Monte_Carlo_tree_search Monte Carlo tree search] (FYI, we are doing the "pure", non-tree version here). | For more background, see [https://en.wikipedia.org/wiki/Monte_Carlo_tree_search Monte Carlo tree search] (FYI, we are doing the "pure", non-tree version here). | ||
Revision as of 12:48, 6 March 2017
Preliminaries
- Make a new project with n = 4 (following these instructions)
- Name your main class "Lab4" (when creating a new module in the instructions above, in the Java class name field)
Instructions
Add TicTacToe.java and TicTacToeBoard.java to your project folder. Make your main() function in Lab4.java look like this:
TicTacToe TTT = new TicTacToe(); TTT.run_tournament();
This will start a "tournament", in which the user can choose an opponent for each game, decide whether to go first or second, or quit. Code is already written to handle the logic of each game, including checking for a winner and preventing invalid moves. Currently there are two pre-written options for opponents: human, in which you will alternate choosing moves manually; and random, in which the computer chooses a random open space on its turn with no intelligence whatsoever.
Your job in this lab is to add a third opponent semi-intelligent option, which we will call Monte Carlo (explained below). Specifically, you will fill in the body of the chooseMonteCarloMove() method in TicTacToeBoard class. You should also add your name in a comment before the class body.
Simple tic-tac-toe AI
The idea behind our Monte Carlo approach to tic-tac-toe move selection is simple. Some moves may lead to a certain win or defeat (either immediately or inevitably), while others simply make victory more or less likely, but not guaranteed. Given a choice of n open spaces on the board, we would like to assess the likelihood of victory or defeat resulting from a first move in each of the n spaces. Whichever candidate move has the highest likelihood of victory is the one we make. Then the opponent makes their move and we repeat the process.
It is not always possible or easy to calculate such a win likelihood exactly, so we try to estimate the likelihood with a probabilistic method.
For more background, see Monte Carlo tree search (FYI, we are doing the "pure", non-tree version here).
Finishing up
Remember to use proper naming and formatting style throughout your code, and submit ONLY TicTacToeBoard.java on Sakai by Friday, March 10
