Merge branch 'v0.9.4'

README.md

@@ -43,7 +43,7 @@ is no concrete timeline for when this will be supported.
 
 ### AI Development
 
-1. Download [Ludii's JAR file](http://ludii.games/downloads.php). This is the
+1. Download [Ludii's JAR file](http://ludii.games/download.php). This is the
 JAR file that can also be used to launch the Ludii application.
 2. Create a new Java project using your favourite IDE. You can also create a
 fork of this [github repository](https://github.com/Ludeme/LudiiExampleAI)
@@ -112,12 +112,12 @@ for visual inspections of your agent's behaviour during development, etc.),
 please cite [our paper on the Ludii system](https://arxiv.org/abs/1905.05013).
 This can be done using the following BibTeX entry:
 
-	@misc{Piette2019Ludii,
-		Author = {{\'E}ric Piette and Dennis J. N. J. Soemers and Matthew Stephenson and Chiara F. Sironi and Mark H. M. Winands and Cameron Browne},
+	@inproceedings{Piette2020Ludii,
+		Author = {{\'E}. Piette and D. J. N. J. Soemers and M. Stephenson and C. F. Sironi and M. H. M. Winands and C. Browne},
 		Title = {Ludii - The Ludemic General Game System},
-		Year = {2019},
-		Eprint = {arXiv:1905.05013},
-		url = {https://arxiv.org/abs/1905.05013}
+		Year = {2020},
+		booktitle = {Proceedings of the 2020 European Conference on Artificial Intelligence},
+		note = {To appear.}
 	}
 
 ## Background Info
@@ -142,6 +142,7 @@ Alternatively, the following email address may be used: `ludii(dot)games(at)gmai
 
 ## Changelog
 
+- 4 July, 2020: Updated repository for compatibility with new version 0.9.4 of Ludii.
 - 3 April, 2020: Updated repository for compatibility with new version 0.6.1 of Ludii.
 - 13 December, 2019: Updated repository for compatibility with new version 0.5.0 of Ludii.
 - 27 November, 2019: Updated repository for compatibility with new version 0.4.1 of Ludii.

src/experiments/RunCustomMatch.java

@@ -4,11 +4,11 @@
 import java.util.List;
 
 import game.Game;
-import player.utils.GameLoader;
 import random.RandomAI;
 import search.mcts.MCTS;
 import util.AI;
 import util.Context;
+import util.GameLoader;
 import util.Trial;
 import util.model.Model;
 

src/experiments/RunLudiiEvalGamesSet.java

@@ -3,11 +3,11 @@
 import java.util.Arrays;
 
 import game.Game;
-import player.experiments.EvalGamesSet;
-import player.utils.GameLoader;
 import random.RandomAI;
 import search.mcts.MCTS;
+import supplementary.experiments.EvalGamesSet;
 import util.AI;
+import util.GameLoader;
 
 /**
  * Example of an experiment that uses Ludii's built-in EvalGamesSet class to

src/experiments/Tutorial.java

@@ -5,14 +5,14 @@
 import java.util.List;
 
 import game.Game;
-import game.types.GameType;
+import game.types.state.GameType;
 import main.FileHandling;
 import main.collections.FastArrayList;
 import mcts.ExampleUCT;
-import player.utils.GameLoader;
 import random.RandomAI;
 import util.AI;
 import util.Context;
+import util.GameLoader;
 import util.Move;
 import util.Trial;
 import util.model.Model;
@@ -62,7 +62,7 @@ public static void main(final String[] args)
 
 		// loop through all the "container states" of the game state
 		// (in many games there is just a board, but there may also be hands, etc.)
-		for (final ContainerState containerState : trial.state().containerStates())
+		for (final ContainerState containerState : context.state().containerStates())
 		{
 			// for every container state we find (often just 1), we'll print a few things:
 
@@ -87,7 +87,7 @@ public static void main(final String[] args)
 		game.apply(context, firstMove);
 
 		// let's print our empty/who/what again, see how they have changed
-		for (final ContainerState containerState : trial.state().containerStates())
+		for (final ContainerState containerState : context.state().containerStates())
 		{
 			System.out.println("Empty locations = " + containerState.emptyChunkSetCell());
 			System.out.println("Who = " + containerState.cloneWhoCell().toChunkString());
@@ -101,7 +101,7 @@ public static void main(final String[] args)
 		game.apply(context, secondMove);
 
 		// let's have a final look at how our state looks after this second move
-		for (final ContainerState containerState : trial.state().containerStates())
+		for (final ContainerState containerState : context.state().containerStates())
 		{
 			System.out.println("Empty locations = " + containerState.emptyChunkSetCell());
 			System.out.println("Who = " + containerState.cloneWhoCell().toChunkString());

src/mcts/ExampleDUCT.java

@@ -11,7 +11,6 @@
 import util.AI;
 import util.Context;
 import util.Move;
-import util.Trial;
 import util.action.Action;
 import utils.AIUtils;
 
@@ -94,14 +93,15 @@ public ExampleDUCT()
 				}
 			}
 
-			Trial trialEnd = current.context.trial();
+			Context contextEnd = current.context;
 
-			if (!trialEnd.over())
+			if (!contextEnd.trial().over())
 			{
 				// Run a playout if we don't already have a terminal game state in node
-				trialEnd = game.playout
+				contextEnd = new Context(contextEnd);
+				game.playout
 				(
-					new Context(current.context), 
+					contextEnd, 
 					null, 
 					-1.0, 
 					null, 
@@ -115,7 +115,7 @@ public ExampleDUCT()
 
 			// This computes utilities for all players at the of the playout,
 			// which will all be values in [-1.0, 1.0]
-			final double[] utilities = AIUtils.utilities(trialEnd.state());
+			final double[] utilities = AIUtils.utilities(contextEnd);
 
 			// Backpropagate utilities through the tree
 			while (current != null)

src/mcts/ExampleUCT.java

@@ -9,7 +9,6 @@
 import util.AI;
 import util.Context;
 import util.Move;
-import util.Trial;
 import utils.AIUtils;
 
 /**
@@ -87,14 +86,15 @@ public ExampleUCT()
 				}
 			}
 
-			Trial trialEnd = current.context.trial();
+			Context contextEnd = current.context;
 
-			if (!trialEnd.over())
+			if (!contextEnd.trial().over())
 			{
 				// Run a playout if we don't already have a terminal game state in node
-				trialEnd = game.playout
+				contextEnd = new Context(contextEnd);
+				game.playout
 				(
-					new Context(current.context), 
+					contextEnd, 
 					null, 
 					-1.0, 
 					null, 
@@ -108,7 +108,7 @@ public ExampleUCT()
 
 			// This computes utilities for all players at the of the playout,
 			// which will all be values in [-1.0, 1.0]
-			final double[] utilities = AIUtils.utilities(trialEnd.state());
+			final double[] utilities = AIUtils.utilities(contextEnd);
 
 			// Backpropagate utilities through the tree
 			while (current != null)