Fixed AMAF, SMAF algorithms.

data/gogame.cfg

@@ -1,4 +1,4 @@
-PlayerOne=ROOT_PAR
+PlayerOne=SMAF
 PlayerTwo=RANDOM
 GUIDelay=1000 	//1 second
 BoardSize=9
@@ -7,4 +7,4 @@ NumGames=1 	//Games for each color per player
 TurnTime=2000 	//seconds per player per turn
 SpectatorBoardShown=true
 WhiteMoveLogged=false
-BlackMoveLogged=false
+BlackMoveLogged=true

src/net/woodyfolsom/msproj/Referee.java

@@ -91,7 +91,6 @@ public class Referee {
 			while (!gameRecord.isFinished()) {
 				GameState gameState = gameRecord.getGameState(gameRecord
 						.getNumTurns());
-				// System.out.println(gameState);
 				
 				Player playerToMove = gameRecord.getPlayerToMove();
 				Policy policy = getPolicy(playerToMove);
@@ -108,6 +107,11 @@ public class Referee {
 				} else {
 					System.out.println("Move rejected - try again.");
 				}
+				
+				if (policy.isLogging())  {
+					System.out.println(gameState);		
+				}
+
 			}
 		} catch (Exception ex) {
 			System.out

src/net/woodyfolsom/msproj/StandAloneGame.java

@@ -13,6 +13,7 @@ import net.woodyfolsom.msproj.gui.Goban;
 import net.woodyfolsom.msproj.policy.HumanGuiInput;
 import net.woodyfolsom.msproj.policy.HumanKeyboardInput;
 import net.woodyfolsom.msproj.policy.MonteCarloAMAF;
+import net.woodyfolsom.msproj.policy.MonteCarloSMAF;
 import net.woodyfolsom.msproj.policy.MonteCarloUCT;
 import net.woodyfolsom.msproj.policy.Policy;
 import net.woodyfolsom.msproj.policy.RandomMovePolicy;
@@ -26,7 +27,7 @@ public class StandAloneGame {
 	private int gameNo = 0;
 
 	enum PLAYER_TYPE {
-		HUMAN, HUMAN_GUI, ROOT_PAR, UCT, RANDOM, RAVE
+		HUMAN, HUMAN_GUI, ROOT_PAR, UCT, RANDOM, RAVE, SMAF
 	};
 
 	public static void main(String[] args) throws IOException {
@@ -41,7 +42,8 @@ public class StandAloneGame {
 					gameSettings.getBoardSize(), gameSettings.getKomi(),
 					gameSettings.getNumGames(), gameSettings.getTurnTime(),
 					gameSettings.isSpectatorBoardShown(),
-					gameSettings.isBlackMoveLogged(), gameSettings.isWhiteMoveLogged());
+					gameSettings.isBlackMoveLogged(),
+					gameSettings.isWhiteMoveLogged());
 			System.out.println("Press <Enter> or CTRL-C to exit");
 			System.in.read(new byte[80]);
 		} catch (IOException ioe) {
@@ -64,14 +66,17 @@ public class StandAloneGame {
 			return PLAYER_TYPE.RANDOM;
 		} else if ("RAVE".equalsIgnoreCase(playerTypeStr)) {
 			return PLAYER_TYPE.RAVE;
+		} else if ("SMAF".equalsIgnoreCase(playerTypeStr)) {
+			return PLAYER_TYPE.SMAF;
 		} else {
 			throw new RuntimeException("Unknown player type: " + playerTypeStr);
 		}
 	}
 
 	public void playGame(PLAYER_TYPE playerType1, PLAYER_TYPE playerType2,
-			int size, double komi, int rounds, long turnLength, boolean showSpectatorBoard,
-			boolean blackMoveLogged, boolean whiteMoveLogged) {
+			int size, double komi, int rounds, long turnLength,
+			boolean showSpectatorBoard, boolean blackMoveLogged,
+			boolean whiteMoveLogged) {
 
 		long startTime = System.currentTimeMillis();
 
@@ -79,28 +84,38 @@ public class StandAloneGame {
 		gameConfig.setKomi(komi);
 
 		Referee referee = new Referee();
-		referee.setPolicy(Player.BLACK,
-				getPolicy(playerType1, gameConfig, Player.BLACK, turnLength, blackMoveLogged));
-		referee.setPolicy(Player.WHITE,
-				getPolicy(playerType2, gameConfig, Player.WHITE, turnLength, whiteMoveLogged));
+		referee.setPolicy(
+				Player.BLACK,
+				getPolicy(playerType1, gameConfig, Player.BLACK, turnLength,
+						blackMoveLogged));
+		referee.setPolicy(
+				Player.WHITE,
+				getPolicy(playerType2, gameConfig, Player.WHITE, turnLength,
+						whiteMoveLogged));
 
 		List<GameResult> round1results = new ArrayList<GameResult>();
 
 		boolean logGameRecords = rounds <= 50;
 		for (int round = 0; round < rounds; round++) {
 			gameNo++;
-			round1results.add(referee.play(gameConfig, gameNo, showSpectatorBoard, logGameRecords));
+			round1results.add(referee.play(gameConfig, gameNo,
+					showSpectatorBoard, logGameRecords));
 		}
 
 		List<GameResult> round2results = new ArrayList<GameResult>();
 
-		referee.setPolicy(Player.BLACK,
-				getPolicy(playerType2, gameConfig, Player.BLACK, turnLength, blackMoveLogged));
-		referee.setPolicy(Player.WHITE,
-				getPolicy(playerType1, gameConfig, Player.WHITE, turnLength, whiteMoveLogged));
+		referee.setPolicy(
+				Player.BLACK,
+				getPolicy(playerType2, gameConfig, Player.BLACK, turnLength,
+						blackMoveLogged));
+		referee.setPolicy(
+				Player.WHITE,
+				getPolicy(playerType1, gameConfig, Player.WHITE, turnLength,
+						whiteMoveLogged));
 		for (int round = 0; round < rounds; round++) {
 			gameNo++;
-			round2results.add(referee.play(gameConfig, gameNo, showSpectatorBoard, logGameRecords));
+			round2results.add(referee.play(gameConfig, gameNo,
+					showSpectatorBoard, logGameRecords));
 		}
 
 		long endTime = System.currentTimeMillis();
@@ -113,13 +128,14 @@ public class StandAloneGame {
 
 			try {
 				if (!logGameRecords) {
-					System.out.println("Each player is set to play more than 50 rounds as each color; omitting individual game .sgf log file output.");
+					System.out
+							.println("Each player is set to play more than 50 rounds as each color; omitting individual game .sgf log file output.");
 				}
 
 				logResults(writer, round1results, playerType1.toString(),
-							playerType2.toString());
+						playerType2.toString());
 				logResults(writer, round2results, playerType2.toString(),
-							playerType1.toString());
+						playerType1.toString());
 				writer.write("Elapsed Time: " + (endTime - startTime) / 1000.0
 						+ " seconds.");
 				System.out.println("Game tournament saved as "
@@ -157,25 +173,38 @@ public class StandAloneGame {
 
 	private Policy getPolicy(PLAYER_TYPE playerType, GameConfig gameConfig,
 			Player player, long turnLength, boolean moveLogged) {
+
+		Policy policy;
+
 		switch (playerType) {
 		case HUMAN:
-			return new HumanKeyboardInput();
+			policy = new HumanKeyboardInput();
+			break;
 		case HUMAN_GUI:
-			return new HumanGuiInput(new Goban(gameConfig, player,""));
+			policy = new HumanGuiInput(new Goban(gameConfig, player, ""));
+			break;
 		case ROOT_PAR:
-			return new RootParallelization(4, turnLength);
+			policy = new RootParallelization(4, turnLength);
+			break;
 		case UCT:
-			return new MonteCarloUCT(new RandomMovePolicy(), turnLength);
+			policy = new MonteCarloUCT(new RandomMovePolicy(), turnLength);
+			break;
+		case SMAF:
+			policy = new MonteCarloSMAF(new RandomMovePolicy(), turnLength, 0);
+			break;
 		case RANDOM:
-			RandomMovePolicy randomMovePolicy = new RandomMovePolicy();
-			randomMovePolicy.setLogging(moveLogged);
-			return randomMovePolicy;
+			policy = new RandomMovePolicy();
+			break;
 		case RAVE:
-			return new MonteCarloAMAF(new RandomMovePolicy(), turnLength);
+			policy = new MonteCarloAMAF(new RandomMovePolicy(), turnLength);
+			break;
 		default:
 			throw new IllegalArgumentException("Invalid PLAYER_TYPE: "
 					+ playerType);
 		}
+
+		policy.setLogging(moveLogged);
+		return policy;
 	}
 
 }

src/net/woodyfolsom/msproj/ann/TTTFilterTrainer.java

@@ -1,6 +1,9 @@
 package net.woodyfolsom.msproj.ann;
 
-import java.io.FileNotFoundException;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.IOException;
 import java.util.ArrayList;
 import java.util.List;
 
@@ -16,66 +19,99 @@ import net.woodyfolsom.msproj.tictactoe.State;
 public class TTTFilterTrainer { // implements epsilon-greedy trainer? online
 								// version of NeuralNetFilter
 
-	public static void main(String[] args) throws FileNotFoundException {
-		double alpha = 0.15;
-		double lambda = .95;
-		int maxGames = 1000;
+	private boolean training = true;
+
+	public static void main(String[] args) throws IOException {
+		double alpha = 0.50;
+		double lambda = 0.90;
+		int maxGames = 100000;
 
 		new TTTFilterTrainer().trainNetwork(alpha, lambda, maxGames);
 	}
 
 	public void trainNetwork(double alpha, double lambda, int maxGames)
-			throws FileNotFoundException {
+			throws IOException {
 
-		FeedforwardNetwork neuralNetwork = new MultiLayerPerceptron(true, 9, 6,
-				1);
-		neuralNetwork.setName("TicTacToe");
-		neuralNetwork.initWeights();
-		TrainingMethod trainer = new TemporalDifference(alpha, lambda);
+		FeedforwardNetwork neuralNetwork;
+		if (training) {
+			neuralNetwork = new MultiLayerPerceptron(true, 9, 9, 1);
+			neuralNetwork.setName("TicTacToe");
+			neuralNetwork.initWeights();
+			TrainingMethod trainer = new TemporalDifference(alpha, lambda);
 
-		System.out.println("Playing untrained games.");
-		for (int i = 0; i < 10; i++) {
-			System.out.println("" + (i + 1) + ". "
-					+ playOptimal(neuralNetwork).getResult());
-		}
-
-		System.out.println("Learning from " + maxGames
-				+ " games of random self-play");
-
-		int gamesPlayed = 0;
-		List<RESULT> results = new ArrayList<RESULT>();
-		do {
-			GameRecord gameRecord = playEpsilonGreedy(0.90, neuralNetwork,
-					trainer);
-			System.out.println("Winner: " + gameRecord.getResult());
-			gamesPlayed++;
-			results.add(gameRecord.getResult());
-		} while (gamesPlayed < maxGames);
-
-		System.out.println("Learned network after " + maxGames
-				+ " training games.");
-
-		for (int i = 0; i < results.size(); i++) {
-			if (i % 10 == 0) {
-				System.out.println("" + (i + 1) + ". " + results.get(i));
+			System.out.println("Playing untrained games.");
+			for (int i = 0; i < 10; i++) {
+				System.out.println("" + (i + 1) + ". "
+						+ playOptimal(neuralNetwork).getResult());
 			}
+
+			System.out.println("Learning from " + maxGames
+					+ " games of random self-play");
+
+			int gamesPlayed = 0;
+			List<RESULT> results = new ArrayList<RESULT>();
+			do {
+				GameRecord gameRecord = playEpsilonGreedy(0.50, neuralNetwork,
+						trainer);
+				System.out.println("Winner: " + gameRecord.getResult());
+				gamesPlayed++;
+				results.add(gameRecord.getResult());
+			} while (gamesPlayed < maxGames);
+
+			System.out.println("Results of every 10th training game:");
+
+			for (int i = 0; i < results.size(); i++) {
+				if (i % 10 == 0) {
+					System.out.println("" + (i + 1) + ". " + results.get(i));
+				}
+			}
+
+			System.out.println("Learned network after " + maxGames
+					+ " training games.");
+		} else {
+			System.out.println("Loading TicTacToe network from file.");
+			neuralNetwork = new MultiLayerPerceptron();
+			FileInputStream fis = new FileInputStream(new File("ttt.net"));
+			if (!new MultiLayerPerceptron().load(fis)) {
+				System.out.println("Error loading ttt.net from file.");
+				return;
+			}
+			fis.close();
 		}
 
 		evalTestCases(neuralNetwork);
 
 		System.out.println("Playing optimal games.");
+		List<RESULT> gameResults = new ArrayList<RESULT>();
 		for (int i = 0; i < 10; i++) {
-			System.out.println("" + (i + 1) + ". "
-					+ playOptimal(neuralNetwork).getResult());
+			gameResults.add(playOptimal(neuralNetwork).getResult());
 		}
 
-		/*
-		 * File output = new File("ttt.net");
-		 * 
-		 * FileOutputStream fos = new FileOutputStream(output);
-		 * 
-		 * neuralNetwork.save(fos);
-		 */
+		boolean suboptimalPlay = false;
+		System.out.println("Optimal game summary: ");
+		for (int i = 0; i < gameResults.size(); i++) {
+			RESULT result = gameResults.get(i);
+			System.out.println("" + (i + 1) + ". " + result);
+			if (result != RESULT.X_WINS) {
+				suboptimalPlay = true;
+			}
+		}
+
+		File output = new File("ttt.net");
+
+		FileOutputStream fos = new FileOutputStream(output);
+
+		neuralNetwork.save(fos);
+
+		System.out.println("Playing optimal vs random games.");
+		for (int i = 0; i < 10; i++) {
+			System.out.println("" + (i + 1) + ". "
+					+ playOptimalVsRandom(neuralNetwork).getResult());
+		}
+
+		if (suboptimalPlay) {
+			System.out.println("Suboptimal play detected!");
+		}
 	}
 
 	private void evalTestCases(FeedforwardNetwork neuralNetwork) {
@@ -115,6 +151,32 @@ public class TTTFilterTrainer { // implements epsilon-greedy trainer? online
 		testNetwork(neuralNetwork, validationSet, inputNames, outputNames);
 	}
 
+	private GameRecord playOptimalVsRandom(FeedforwardNetwork neuralNetwork) {
+		GameRecord gameRecord = new GameRecord();
+
+		Policy neuralNetPolicy = new NeuralNetPolicy(neuralNetwork);
+		Policy randomPolicy = new RandomPolicy();
+
+		State state = gameRecord.getState();
+
+		Policy[] policies = new Policy[] { neuralNetPolicy, randomPolicy };
+		int turnNo = 0;
+		do {
+			Action action;
+			State nextState;
+
+			action = policies[turnNo % 2].getAction(gameRecord.getState());
+
+			nextState = gameRecord.apply(action);
+			System.out.println("Action " + action + " selected by policy "
+					+ policies[turnNo % 2].getName());
+			System.out.println("Next board state: " + nextState);
+			state = nextState;
+			turnNo++;
+		} while (!state.isTerminal());
+		return gameRecord;
+	}
+
 	private GameRecord playOptimal(FeedforwardNetwork neuralNetwork) {
 		GameRecord gameRecord = new GameRecord();
 
@@ -122,8 +184,6 @@ public class TTTFilterTrainer { // implements epsilon-greedy trainer? online
 
 		State state = gameRecord.getState();
 
-		System.out.println("Playing optimal game:");
-		
 		do {
 			Action action;
 			State nextState;
@@ -131,14 +191,12 @@ public class TTTFilterTrainer { // implements epsilon-greedy trainer? online
 			action = neuralNetPolicy.getAction(gameRecord.getState());
 
 			nextState = gameRecord.apply(action);
-			System.out.println("Action " + action + " selected by policy " +
-					neuralNetPolicy.getName());
+			System.out.println("Action " + action + " selected by policy "
+					+ neuralNetPolicy.getName());
 			System.out.println("Next board state: " + nextState);
 			state = nextState;
 		} while (!state.isTerminal());
 
-		// finally, reinforce the actual reward
-
 		return gameRecord;
 	}
 
@@ -196,7 +254,7 @@ public class TTTFilterTrainer { // implements epsilon-greedy trainer? online
 		for (int valIndex = 0; valIndex < validationSet.length; valIndex++) {
 			NNDataPair dp = new NNDataPair(new NNData(inputNames,
 					validationSet[valIndex]), new NNData(outputNames,
-					new double[] {0.0}));
+					new double[] { 0.0 }));
 			System.out.println(dp + " => " + neuralNetwork.compute(dp));
 		}
 	}

src/net/woodyfolsom/msproj/ann/TemporalDifference.java

@@ -4,7 +4,7 @@ import java.util.List;
 
 public class TemporalDifference extends TrainingMethod {
 	private final double alpha;
-	private final double gamma = 1.0;
+	// private final double gamma = 1.0;
 	private final double lambda;
 
 	public TemporalDifference(double alpha, double lambda) {
@@ -81,23 +81,27 @@ public class TemporalDifference extends TrainingMethod {
 		}
 	}
 
-	private void updateWeights(FeedforwardNetwork neuralNetwork, double predictionError) {
+	private void updateWeights(FeedforwardNetwork neuralNetwork,
+			double predictionError) {
 		for (Connection connection : neuralNetwork.getConnections()) {
-			/*Neuron srcNeuron = neuralNetwork.getNeuron(connection.getSrc());
-			Neuron destNeuron = neuralNetwork.getNeuron(connection.getDest());
-			
-			double delta = alpha * srcNeuron.getOutput()
-					* destNeuron.getGradient() * predictionError + connection.getTrace() * lambda;
-			
-			// TODO allow for momentum
-			// double lastDelta = connection.getLastDelta();
-			connection.addDelta(delta);*/
+			/*
+			 * Neuron srcNeuron = neuralNetwork.getNeuron(connection.getSrc());
+			 * Neuron destNeuron =
+			 * neuralNetwork.getNeuron(connection.getDest());
+			 * 
+			 * double delta = alpha * srcNeuron.getOutput()
+			 * destNeuron.getGradient() * predictionError +
+			 * connection.getTrace() * lambda;
+			 * 
+			 * // TODO allow for momentum // double lastDelta =
+			 * connection.getLastDelta(); connection.addDelta(delta);
+			 */
 			Neuron srcNeuron = neuralNetwork.getNeuron(connection.getSrc());
 			Neuron destNeuron = neuralNetwork.getNeuron(connection.getDest());
 			double delta = alpha * srcNeuron.getOutput()
 					* destNeuron.getGradient() + connection.getTrace() * lambda;
-			//TODO allow for momentum
-			//double lastDelta = connection.getLastDelta();
+			// TODO allow for momentum
+			// double lastDelta = connection.getLastDelta();
 			connection.addDelta(delta);
 		}
 	}
@@ -117,23 +121,23 @@ public class TemporalDifference extends TrainingMethod {
 	@Override
 	protected void iteratePattern(FeedforwardNetwork neuralNetwork,
 			NNDataPair statePair, NNData nextReward) {
-		//System.out.println("Learningrate: " + alpha);
+		// System.out.println("Learningrate: " + alpha);
 
 		zeroGradients(neuralNetwork);
 
-		//System.out.println("Training with: " + statePair.getInput());
+		// System.out.println("Training with: " + statePair.getInput());
 
 		NNData ideal = nextReward;
 		NNData actual = neuralNetwork.compute(statePair);
 
-		//System.out.println("Updating weights.  Ideal Output: " + ideal);
-		//System.out.println("Actual Output: " + actual);
+		// System.out.println("Updating weights.  Ideal Output: " + ideal);
+		// System.out.println("Actual Output: " + actual);
 
 		// backpropagate the gradients w.r.t. output error
 		backPropagate(neuralNetwork, ideal);
 
 		double predictionError = statePair.getIdeal().getValues()[0] // reward_t
-								+ actual.getValues()[0] - nextReward.getValues()[0];
+				+ actual.getValues()[0] - nextReward.getValues()[0];
 
 		updateWeights(neuralNetwork, predictionError);
 	}

src/net/woodyfolsom/msproj/policy/AlphaBeta.java

@@ -16,6 +16,15 @@ public class AlphaBeta implements Policy {
 
 	private final ValidMoveGenerator validMoveGenerator = new ValidMoveGenerator();
 
+	private boolean logging = false;
+	public boolean isLogging() {
+		return logging;
+	}
+
+	public void setLogging(boolean logging) {
+		this.logging = logging;
+	}
+
 	private int lookAhead;
 	private int numStateEvaluations = 0;
 

src/net/woodyfolsom/msproj/policy/HumanGuiInput.java

@@ -9,6 +9,15 @@ import net.woodyfolsom.msproj.Player;
 import net.woodyfolsom.msproj.gui.Goban;
 
 public class HumanGuiInput implements Policy {
+	private boolean logging;
+	public boolean isLogging() {
+		return logging;
+	}
+
+	public void setLogging(boolean logging) {
+		this.logging = logging;
+	}
+
 	private Goban goban;
 	
 	public HumanGuiInput(Goban goban) {

src/net/woodyfolsom/msproj/policy/HumanKeyboardInput.java

@@ -9,6 +9,15 @@ import net.woodyfolsom.msproj.GameState;
 import net.woodyfolsom.msproj.Player;
 
 public class HumanKeyboardInput implements Policy {
+	private boolean logging = false;
+	
+	public boolean isLogging() {
+		return logging;
+	}
+
+	public void setLogging(boolean logging) {
+		this.logging = logging;
+	}
 
 	@Override
 	public Action getAction(GameConfig gameConfig, GameState gameState,

src/net/woodyfolsom/msproj/policy/Minimax.java

@@ -16,6 +16,15 @@ public class Minimax implements Policy {
 
 	private final ValidMoveGenerator validMoveGenerator = new ValidMoveGenerator();
 
+	private boolean logging = false;
+	public boolean isLogging() {
+		return logging;
+	}
+
+	public void setLogging(boolean logging) {
+		this.logging = logging;
+	}
+
 	private int lookAhead;
 	private int numStateEvaluations = 0;
 

src/net/woodyfolsom/msproj/policy/MonteCarlo.java

@@ -15,6 +15,15 @@ import net.woodyfolsom.msproj.tree.MonteCarloProperties;
 public abstract class MonteCarlo implements Policy {
 	protected static final int ROLLOUT_DEPTH_LIMIT = 250;
 
+	private boolean logging = false;
+	public boolean isLogging() {
+		return logging;
+	}
+
+	public void setLogging(boolean logging) {
+		this.logging = logging;
+	}
+
 	protected int numStateEvaluations = 0;
 	protected Policy movePolicy;
 

src/net/woodyfolsom/msproj/policy/MonteCarloAMAF.java

@@ -63,6 +63,43 @@ public class MonteCarloAMAF extends MonteCarloUCT {
 				rootGameState, new AMAFProperties());
 	}
 	
+	@Override
+	public Action getBestAction(GameTreeNode<MonteCarloProperties> node) {
+		Action bestAction = Action.NONE;
+		double bestScore = Double.NEGATIVE_INFINITY;
+		GameTreeNode<MonteCarloProperties> bestChild = null;
+		
+		for (Action action : node.getActions()) {		
+			GameTreeNode<MonteCarloProperties> childNode = node
+					.getChild(action);
+
+			AMAFProperties childProps = (AMAFProperties)childNode.getProperties();
+			double childScore = childProps.getAmafWins() / (double)childProps.getAmafVisits();
+			
+			if (childScore >= bestScore) {
+				bestScore = childScore;
+				bestAction = action;
+				bestChild = childNode;
+			}
+		}
+
+		if (bestAction == Action.NONE) {
+			System.out
+					.println("MonteCarloUCT failed - no actions were found for the current game state (not even PASS).");
+		} else {
+			System.out.println("Action " + bestAction + " selected for "
+					+ node.getGameState().getPlayerToMove()
+					+ " with simulated win ratio of "
+					+ (bestScore * 100.0 + "%"));
+			System.out.println("It was visited "
+					+ bestChild.getProperties().getVisits() + " times out of "
+					+ node.getProperties().getVisits() + " rollouts among "
+					+ node.getNumChildren()
+					+ " valid actions from the current state.");
+		}
+		return bestAction;
+	}
+	
 	@Override
 	protected double getNodeScore(GameTreeNode<MonteCarloProperties> gameTreeNode) {
 		//double nodeVisits = gameTreeNode.getParent().getProperties().getVisits();
@@ -72,16 +109,8 @@ public class MonteCarloAMAF extends MonteCarloUCT {
 		if (gameTreeNode.getGameState().isTerminal()) {
 			nodeScore = 0.0;
 		} else {
-			/*
-			MonteCarloProperties properties = gameTreeNode.getProperties();
-			nodeScore = (double) (properties.getWins() / properties
-					.getVisits())
-					+ (TUNING_CONSTANT * Math.sqrt(Math.log(nodeVisits)
-							/ gameTreeNode.getProperties().getVisits()));
-							 * 
-							 */
 			AMAFProperties properties = (AMAFProperties) gameTreeNode.getProperties();
-			nodeScore = (double) (properties.getAmafWins() / properties
+			nodeScore = (properties.getAmafWins() / (double) properties
 					.getAmafVisits())
 					+ (TUNING_CONSTANT * Math.sqrt(Math.log(parentAmafVisits)
 							/ properties.getAmafVisits()));

src/net/woodyfolsom/msproj/policy/MonteCarloSMAF.java

@@ -0,0 +1,59 @@
+package net.woodyfolsom.msproj.policy;
+
+import java.util.List;
+
+import net.woodyfolsom.msproj.Action;
+import net.woodyfolsom.msproj.Player;
+import net.woodyfolsom.msproj.tree.AMAFProperties;
+import net.woodyfolsom.msproj.tree.GameTreeNode;
+import net.woodyfolsom.msproj.tree.MonteCarloProperties;
+
+public class MonteCarloSMAF extends MonteCarloAMAF {	
+	private int horizon;
+	
+	public MonteCarloSMAF(Policy movePolicy, long searchTimeLimit, int horizon) {
+		super(movePolicy, searchTimeLimit);
+		this.horizon = horizon;
+	}
+
+	@Override
+	public void update(GameTreeNode<MonteCarloProperties> node, Rollout rollout) {
+		GameTreeNode<MonteCarloProperties> currentNode = node;
+		//List<Action> subTreeActions = new ArrayList<Action>(rollout.getPlayout());
+		
+		List<Action> playout = rollout.getPlayout();
+		int reward = rollout.getReward();
+		while (currentNode != null) {
+			AMAFProperties nodeProperties = (AMAFProperties)currentNode.getProperties();
+			
+			//Always update props for the current node
+			nodeProperties.setWins(nodeProperties.getWins() + reward);
+			nodeProperties.setVisits(nodeProperties.getVisits() + 1);
+			nodeProperties.setAmafWins(nodeProperties.getAmafWins() + reward);
+			nodeProperties.setAmafVisits(nodeProperties.getAmafVisits() + 1);
+			
+			GameTreeNode<MonteCarloProperties> parentNode = currentNode.getParent();
+			if (parentNode != null) {
+				Player playerToMove = parentNode.getGameState().getPlayerToMove();
+				for (Action actionFromParent : parentNode.getActions()) {
+					if (playout.subList(0, Math.max(horizon,playout.size())).contains(actionFromParent)) {
+						GameTreeNode<MonteCarloProperties> subTreeChild = parentNode.getChild(actionFromParent);
+						//Don't count AMAF properties for the current node twice
+						if (subTreeChild == currentNode) {
+							continue;
+						}
+						
+						AMAFProperties siblingProperties = (AMAFProperties)subTreeChild.getProperties();
+						//Only update AMAF properties if the sibling is reached by the same action with the same player to move
+						if (rollout.hasPlay(playerToMove,actionFromParent)) {
+							siblingProperties.setAmafWins(siblingProperties.getAmafWins() + reward);
+							siblingProperties.setAmafVisits(siblingProperties.getAmafVisits() + 1);
+						}
+					}
+				}
+			}
+
+			currentNode = currentNode.getParent();
+		}
+	}
+}

src/net/woodyfolsom/msproj/policy/MonteCarloUCT.java

@@ -90,11 +90,8 @@ public class MonteCarloUCT extends MonteCarlo {
 			GameTreeNode<MonteCarloProperties> childNode = node
 					.getChild(action);
 
-			//MonteCarloProperties properties = childNode.getProperties();
-			//double childScore = (double) properties.getWins()
-			//		/ properties.getVisits();
-
-			double childScore = getNodeScore(childNode);
+			MonteCarloProperties childProps = childNode.getProperties();
+			double childScore = childProps.getWins() / (double)childProps.getVisits();
 			
 			if (childScore >= bestScore) {
 				bestScore = childScore;

src/net/woodyfolsom/msproj/policy/Policy.java

@@ -17,4 +17,8 @@ public interface Policy {
 	public int getNumStateEvaluations();
 	
 	public void setState(GameState gameState);
+
+	boolean isLogging();
+	
+	void setLogging(boolean logging);
 }

src/net/woodyfolsom/msproj/policy/RandomMovePolicy.java

@@ -110,6 +110,7 @@ public class RandomMovePolicy implements Policy, ActionGenerator {
 		return randomAction;
 	}
 
+	@Override
 	public boolean isLogging() {
 		return logging;
 	}

src/net/woodyfolsom/msproj/policy/RootParallelization.java

@@ -13,7 +13,16 @@ import net.woodyfolsom.msproj.Player;
 import net.woodyfolsom.msproj.tree.MonteCarloProperties;
 
 public class RootParallelization implements Policy {
+	private boolean logging = false;
 	private int numTrees = 1;
+	public boolean isLogging() {
+		return logging;
+	}
+
+	public void setLogging(boolean logging) {
+		this.logging = logging;
+	}
+
 	private long timeLimit = 1000L;
 
 	public RootParallelization(int numTrees, long timeLimit) {