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public:t-622-arti-16-1:programming_assignment_2_-_breakthrough [2016/02/12 16:23] – [Programming Assignment 2 - Adversarial Search] stephanpublic:t-622-arti-16-1:programming_assignment_2_-_breakthrough [2024/04/29 13:33] (current) – external edit 127.0.0.1
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 The game is played on an grid if width W (<latex>$2 \leq W \leq 10$</latex>) and height H (<latex>$4 \leq H \leq 10$</latex>). The initial state is setup such that both players have two rows of pawns of their respective color. White's paws are on rows 1 and 2 while black's pawns are on rows H-1 and H. The two players "white" and "black" take turns in moving one of their pawns. White moves first. Pawns move like in chess, that is, they can move one spot straight forward (up for white or down for black) onto an empty square or they can capture an opponents piece by moving one spot diagonally forward. As opposed to chess, pawns on the second row can not move two spaces at once. The goal of the game is to advance any one pawn to the opposite side of the board (i.e., to promote a pawn). The game ends, if one of the players has reached his goal or if the player who's turn it is does not have any legal move. This can happen if he does not have any pieces left, or if all his pawns are in positions where they cannot move. The game ends in a draw if none of the players wins. The scores are 100 points for winning, 50 points for a draw and 0 points for losing. The game is played on an grid if width W (<latex>$2 \leq W \leq 10$</latex>) and height H (<latex>$4 \leq H \leq 10$</latex>). The initial state is setup such that both players have two rows of pawns of their respective color. White's paws are on rows 1 and 2 while black's pawns are on rows H-1 and H. The two players "white" and "black" take turns in moving one of their pawns. White moves first. Pawns move like in chess, that is, they can move one spot straight forward (up for white or down for black) onto an empty square or they can capture an opponents piece by moving one spot diagonally forward. As opposed to chess, pawns on the second row can not move two spaces at once. The goal of the game is to advance any one pawn to the opposite side of the board (i.e., to promote a pawn). The game ends, if one of the players has reached his goal or if the player who's turn it is does not have any legal move. This can happen if he does not have any pieces left, or if all his pawns are in positions where they cannot move. The game ends in a draw if none of the players wins. The scores are 100 points for winning, 50 points for a draw and 0 points for losing.
  
-The legal moves for the agent are called "(MOVE x1 y1 x2 y2)" meaning that the pawn at x1,y1 is moved to x2,y2. x1,x2 are integers between 1 and W. y1,y2 are integers between 1 and H.+The legal moves for the agent are called "(move x1 y1 x2 y2)" meaning that the pawn at x1,y1 is moved to x2,y2. x1,x2 are integers between 1 and W. y1,y2 are integers between 1 and H. 
  
  
-/* 
 ===== Tournament ===== ===== Tournament =====
-The agents that were created in class participated in a tournament against each other. The results of the tournament can be seen [[http://ggpserver.general-game-playing.de/ggpserver/public/view_tournament.jsp?tournamentID=ai15_c4&sortBy=averageReward&sortOrder=desc|here]].+The agents that were created in class participated in a tournament against each other. 
 +There were 16 agents competing in the first round. In that round, every agent played on the 3x5 and the 5x5 boards against everyone else, once as white and once as black. 
 +The top 7 of the first round  (because there was a distinctive gap between 7th and 8th place) got to compete in a final round on the 8x8 board. 
 + 
 +The results of the first round can be seen [[http://ggpserver.general-game-playing.de/ggpserver/public/view_tournament.jsp?tournamentID=ru_ai16_bt&sortBy=averageReward&sortOrder=desc|here]]. 
 +The results of the final round can be seen [[http://ggpserver.general-game-playing.de/ggpserver/public/view_tournament.jsp?tournamentID=ru_ai16_bt2&sortBy=averageReward&sortOrder=desc|here]].
  
 ^Group^Player Name^Average Score^ ^Group^Player Name^Average Score^
-|Ari Freyr Ásgeirsson |ai15_c4_1   || +|Fanney Sigurðardóttir   |ai16_bt_10   | (82.76) FINALIST - **79.2** | 
-|Ævar Ísak Ástþórsson |:::|:::| +|Guðni Fannar Kristjánsson   |:::|:::| 
-|Ásgeir Viðar Árnason |ai15_c4_2   || +|Hrafn Orri Hrafnkelsson   |:::|:::| 
-|Fabio Alessandrelli  |:::|:::| +|Kristinn Þorri Þrastarson   |:::|:::| 
-|Atli Sævar Guðmundsson  |ai15_c4_3   || +|Guðmundur Harðarson |ai16_bt_2   | (76.72) FINALIST - **75.0** | 
-|Ægir Már Jónsson     |:::|:::| +|Andri Ívarsson  |:::|:::| 
-|Stefanía Bergljót Stefánsdóttir     |:::|:::| +|Björn Ingi Baldvinsson  |:::|:::| 
-|Arnar Freyr Bjarnason     |:::|:::| +|Andri Már Þórhallsson   |ai16_bt_4   | (74.14) FINALIST - **62.5** | 
-|Bjarni Egill   |ai15_c4_4   || +|Ásgeir Þór Másson   |:::|:::| 
-|Davíð Hafþór   |:::|:::| +|Karl Ingi Karlsson   |:::|:::| 
-|Einar Karl   |:::|:::| +|Sindri Már Kaldal Sigurjónsson   |ai16_bt_15   | (70.69) FINALIST - **58.3** | 
-|Ólafur Ingi Eiríksson   |:::|:::| +|Eysteinn Gunnlaugsson   |:::|:::| 
-|Freyr Bergsteinsson   |ai15_c4_5   || +|Magnús Sigurðarson   |:::|:::| 
-|Kristinn Þröstur Sigurðarson   |:::|:::| +|Brynjar Ólafsson   |ai16_bt_5   | (62.93) FINALIST - **54.2** | 
-|Gabríel Arthúr Pétursson   |ai15_c4_6   || +|Tryggvi Þór Guðmundsson   |:::|:::| 
-|Kristján Árni Gerhardsson   |:::|:::| +|Hörður Már Hafsteinsson   |:::|:::| 
-|Ingibergur Sindri Stefnisson   |:::|:::| +|Ari Þórðarson   |:::|:::| 
-|Geir Ingi Sigurðsson   |ai15_c4_7   || +|Eva Segarra Raro   |ai16_bt_9   | (64.66) FINALIST - **12.5** | 
-|Daníel Þór Gunnlaugsson   |:::|:::| +|Jakub Mackovic   |:::|:::| 
-|Guðlaugur Garðar Eyþórsson   |ai15_c4_8   || +|Roser Sanchez Todo   |:::|:::| 
-|Guðmundur Stefánsson   |ai15_c4_9   || +|Kári Eiríksson  |ai16_bt_3   | (66.38) FINALIST - **8.3** | 
-|Hinrik Már Hreinsson   |ai15_c4_10   || +|Magnús Vilhelm Björnsson     |:::|:::| 
-|Svanhvít Jónsdóttir   |:::|:::| +|Andri Már Ómarsson     |:::|:::| 
-|Andri Már Sigurðsson   |:::|:::| +|Sverrir Magnússon     |:::|:::| 
-|Hafdís Erla Helgadóttir   |:::|:::| +|Eiður Sveinn Gunnarsson   |ai16_bt_8   | 48.28 | 
-|Ingólfur Halldórsson   |ai15_c4_11   || +|Orri Ólafsson   |:::|:::| 
-|Ragnar Adolf Árnason   |:::|:::| +|Jóhannes Páll Magnússon   |:::|:::| 
-|Kjartan Valur Kjartansson   |ai15_c4_12   || +|Ingimar Örn Oddsson |ai16_bt_1   | 44.83 | 
-|Murray Tannock   |ai15_c4_13   || +|Alexander Björnsson |:::|:::| 
-|Páll Arinbjarnar   |ai15_c4_14   || +|Johan Ejstrud |:::|:::| 
-|Davíð Arnar Sverrisson   |:::|:::| +|Kristján Hreinn Bergsson   |ai16_bt_14   | 41.38 | 
-|Stefán Ólafsson   |ai15_c4_15   || +|Egill Anton Hlöðversson   |ai16_bt_13   | 40.52 | 
-|Sveinn Henrik Kristinsson   |ai15_c4_16   || +|Jón Gísli Björgvinsson   |:::|:::| 
-|Andri Fannar Freysson   |:::|:::| +|Atli Freyr Einarsson   |ai16_bt_7   | 26.72 | 
-|Sigurjón Rúnar Vikarsson   |:::|:::| +|Guðjón Geir Jónsson   |:::|:::| 
-|Ólafur Helgi Jónsson   |:::|:::|+|Gunnar Karl Pálmason   |:::|:::| 
 +|Steinar Ágúst Steinarsson   |:::|:::| 
 +|Grímur Kristinsson   |ai16_bt_11   | 25.86 | 
 +|Natan Örn Ólafsson   |:::|:::| 
 +|Sölvi Hjaltason   |:::|:::| 
 +|Kristján Harðarson   |:::|:::| 
 +|Guðrún Inga Baldursdóttir   |ai16_bt_12   | 24.14 | 
 +|Björn Ingemar Elfström   |:::|:::| 
 +|Davíð Freyr Jónsson   |:::|:::| 
 +|Quang Van Nguyen   |ai16_bt_6   | 20.69 | 
 +|Einar Hallberg Ragnarsson   |:::|:::| 
 +|Ásgeir Frímannsson   |:::|:::| 
 +|Janus Þór Kristjánsson   |:::|:::| 
 +|Tinna Frímann Jökulsdóttir   |ai16_bt_16   | 10.0 | 
 +|Dagur Arinbjörn Daníelsson   |:::|:::|
 ^^^^ ^^^^
-*/+
  
 ===== Tasks ===== ===== Tasks =====
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   - Improve the state evaluation function or implement a better one.   - Improve the state evaluation function or implement a better one.
   - Test if it is really better by pitching two agents (one with each evaluation function) against each other or by pitching each evaluation function against a random agent. If you run the experiments with the random agent, you need to repeat the experiment a decent number of times to get significant results. Don't forget to switch sides because white has an advantage in the game.   - Test if it is really better by pitching two agents (one with each evaluation function) against each other or by pitching each evaluation function against a random agent. If you run the experiments with the random agent, you need to repeat the experiment a decent number of times to get significant results. Don't forget to switch sides because white has an advantage in the game.
-  - Do all experiments with time constraints (play clock) of 1s, 5s and 10s. +  - Run the experiments with different board sizes and time constraints (play clock) of 1s and 10s. 
-  - Make your code fast! The more state expansions you get per second, the better the player. Ideally, you should be able to solve the +  - Make your code fast! The more state expansions you get per second, the better the player. Ideally, you should be able to solve the small boards (3x5, 5x5) in 10s.
  
 ===== Material ===== ===== Material =====
-  * java project for the agent development+  * {{:public:t-622-arti-16-1:breakthrough_agent.zip|java project for the agent development}}
  
-The files in the archive are similar to those in the first programming assignment. The archive contains code for implementing an agent in the src directory. The agent is actually a server process which listens on some port and waits for the real robot or a simulator to send a message. It will then reply with the next action the agent wants to execute. +The files in the archive are similar to those in the first programming assignment. The archive contains code for implementing an agent in the src directory. The agent is actually a server process which listens on some port and waits for a game simulator or a game playing robot to send a message. It will then reply with the next action the agent wants to execute. 
-/* + 
-The zip file also contains the description of the environment (connect4.gdl) and a simulator (gamecontroller-gui.jar). To test your agent: +The zip file also contains the description of the environment for different board sizes (breakthrough_XxY.gdl) and a two simulators (gamecontroller-gui.jar and kiosk.jar). To test your agent: 
-  * Start the simulator (execute gamecontroller-gui.jar with either double-click or using the command "java -jar gamecontroller-gui.jar" on the command line).+  * Start the simulator (execute gamecontroller-gui.jar with either double-click or using the command "java -jar gamecontroller-gui.jar" on the command line)
   * Setup the simulator as shown in this picture:   * Setup the simulator as shown in this picture:
-    {{:public:t-622-arti-12-1:gamecontroller-settings-connect4.png?nolink&|}}+  * {{:public:t-622-arti-16-1:gamecontroller_settings.png?nolink|}}
   * You can use your player as both the first and the second role of the game, just not at the same time. To let two instances of your agent play against each other, start your agent twice with different ports to listen on and use the respective ports in the simulator.   * You can use your player as both the first and the second role of the game, just not at the same time. To let two instances of your agent play against each other, start your agent twice with different ports to listen on and use the respective ports in the simulator.
   * Run the "Main" class in the project. If you added your own agent class, make sure that it is used in the main method of Main.java. You can also execute the "ant run" on the command line, if you have [[http://ant.apache.org/|Ant]] installed. The output of the agent should say "NanoHTTPD is listening on port 4001", which indicates that your agent is ready and waiting for messages to arrive on the specified port.   * Run the "Main" class in the project. If you added your own agent class, make sure that it is used in the main method of Main.java. You can also execute the "ant run" on the command line, if you have [[http://ant.apache.org/|Ant]] installed. The output of the agent should say "NanoHTTPD is listening on port 4001", which indicates that your agent is ready and waiting for messages to arrive on the specified port.
-  * Now push the "Start" button in the simulator and your agent should get some messages and reply with the actions it wants to execute. At the end, the output of the simulator tells you how many points both players got: "Game over! results: 0 100", the first number is for white and the second for red.+  * Now push the "Start" button in the simulator and your agent should get some messages and reply with the actions it wants to execute. At the end, the output of the simulator tells you how many points both players got: "Game over! results: 0 100", the first number is for white and the second for black.
   * If the output of the simulator contains any line starting with "SEVERE", something is wrong. The two most common problems are the network connection (e.g., due to a firewall) between the simulator and the agent or the agent sending illegal moves.   * If the output of the simulator contains any line starting with "SEVERE", something is wrong. The two most common problems are the network connection (e.g., due to a firewall) between the simulator and the agent or the agent sending illegal moves.
-*/+  * You can also use the kiosk to play against your agent. The kiosk does have visualization of the game, but it does not allow you to let two instances of the agent play against each other.
 ===== Hints ===== ===== Hints =====
 For implementing your agent: For implementing your agent:
   * Add a new class that implements the "Agent" interface. Look at RandomAgent.java to see how this is done.   * Add a new class that implements the "Agent" interface. Look at RandomAgent.java to see how this is done.
-  * You have to implement the methods "init" and "nextAction". "init" will be called once at the start and should be used to initialize the agent. You will get the information, which role your agent is playing (white or red) and how much time the agent has for computing each move. "nextAction" gets the previous move as input and has to return the next action the agent is supposed to execute within the given time limit. "nextAction" is called for every step of the game. If it is not your players turn return "NOOP".+  * You have to implement the methods "init" and "nextAction". "init" will be called once at the start and should be used to initialize the agent. You will get the information, which role your agent is playing (white or black) and how much time the agent has for computing each move. "nextAction" gets the previous move as input and has to return the next action the agent is supposed to execute within the given time limit. "nextAction" is called for every step of the game. If it is not your players turn return "NOOP".
   * **Make sure your agent is able to play both roles (white and black)!**   * **Make sure your agent is able to play both roles (white and black)!**
   * You can make sure to be on time by regularly checking whether there is time left during the search process and stopping the search just before you run out of time, e.g., by throwing an exception that you catch where you call the search function the first time.   * You can make sure to be on time by regularly checking whether there is time left during the search process and stopping the search just before you run out of time, e.g., by throwing an exception that you catch where you call the search function the first time.
-  * To specify the port your agent is running on change the build.xml file as follows and use the command line ''ant -Darg0=PORT run'' with PORT being the port number:+  * Your agent should be able to play the small boards perfectly (at least the 3x5, probably also the 5x5), with a playclock of around 10 seconds. You will need a decent heuristics to play the bigger boards well. 
 +  * To specify the port your agent is running on change the build.xml file as follows and use the command line ''ant -Dport=PORT run'' with PORT being the port number:
 <file xml build.xml> <file xml build.xml>
       ...       ...
       <target name="run" depends="dist">       <target name="run" depends="dist">
               <java jar="${dist}/${projectname}.jar" fork="true">               <java jar="${dist}/${projectname}.jar" fork="true">
-                      <arg value="${arg0}"/> <!-- add this line here! -->+                      <arg value="${port}"/> <!-- add this line here! -->
                       <jvmarg value="-Xmx500m" />                       <jvmarg value="-Xmx500m" />
               </java>                       </java>        
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 ===== Handing In ===== ===== Handing In =====
 Please hand in a zip file containing: Please hand in a zip file containing:
-  * the source code and an executable jar file for your agent +  * the complete src folder containing the source code for your agent, but also the other classes that came with the project 
-  * a short description of your heuristic +  * a pdf with a short description of your heuristic and the results of the experiments and the conclusions you draw from them
-  * the results of the experiments and which conclusions you draw from them +
-To create the zip file you just edit the "student" property in the build.xml and call the "zip" target using ant (execute "ant zip" in the directory containing the build.xml). Make sure to have all files that are to be in the zip file in the directory containing the build.xml or below.+
  
-The deadline is TBA. We will have a tournament between your agents afterwards. Extra points for the top players!+The deadline is 29.02.2016. We will have a tournament between your agents afterwards. Extra points for the top players!
  
 /* You can watch the tournament [[http://130.208.241.192/ggpserver/public/view_tournament.jsp?tournamentID=ru_arti13_connect4|here]]. /* You can watch the tournament [[http://130.208.241.192/ggpserver/public/view_tournament.jsp?tournamentID=ru_arti13_connect4|here]].
 */ */
- 
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