Substantial refactoring to implement correct Naive, UCT Monte Carlo tree search methods.

Removed unnecessary distinction between policy and tree search (tree search is a special kind of policy).
Calculation of all valid moves / arbitrary sets of moves is now a seperate class, as it serves a different purpose than a policy.
Introduced regression error in AlphaBeta test.

test/net/woodyfolsom/msproj/policy/MinimaxTest.java

@@ -0,0 +1,34 @@
+package net.woodyfolsom.msproj.policy;
+
+import static org.junit.Assert.assertEquals;
+
+import net.woodyfolsom.msproj.GameBoard;
+import net.woodyfolsom.msproj.GameConfig;
+import net.woodyfolsom.msproj.GameState;
+import net.woodyfolsom.msproj.policy.Policy;
+
+import org.junit.Test;
+
+
+public class MinimaxTest {
+	
+	@Test
+	public void testGenmove() {
+		Policy moveGenerator = new Minimax();
+		GameState gameState = new GameState(5);
+		gameState.playStone('A', 2, GameBoard.BLACK_STONE);
+		gameState.playStone('B', 1, GameBoard.BLACK_STONE);
+		gameState.playStone('C', 2, GameBoard.BLACK_STONE);
+		gameState.playStone('B', 4, GameBoard.BLACK_STONE);
+		
+		String move = moveGenerator.getAction(new GameConfig(), gameState, "w");
+		System.out.println("Generated move: " + move);
+		gameState.playStone("w", move);
+		
+		System.out.println(gameState);
+		
+		assertEquals(Policy.PASS,moveGenerator.getAction(new GameConfig(), gameState, "?"));
+		
+		System.out.println(gameState);
+	}
+}