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--- public:t-622-arti-12-1:lab_1_-_agents [2012/01/19 12:33] – created stephan
+++ public:t-622-arti-12-1:lab_1_-_agents [2024/04/29 13:33] (current) – external edit 127.0.0.1
@@ Line 15: / Line 15: @@
 ===== Tasks =====
-  - Develop a simple reflex vacuum cleaner agent. Is it rational?
+  - Develop a simple reflex vacuum cleaner agent. Is it rational? (Ok, this is a bit of a trick task. You should find out, that it is not actually possible to implement this with a simple reflex agent.)
   - Develop a model-based reflex agent that is able to clean every cell. Is it rational?
+To make it a bit easier you can use the following assumptions:
+  * The room is rectangular. It has only 4 straight walls that meet at right angles. There are no obstacles in the room. That is, the strategy "Go until you bump into a wall then turn right and repeat" will make the agent walk straight to a wall and then around the room along the wall.
+  * The room is fairly small, so that 100 actions are enough to visit every cell, suck all the dirt and return home given a halfway decent algorithm.
+===== Material =====
+  * {{:public:t-622-arti-12-1:vacuumcleaner.zip|java project for the agent development}}
+  * {{:public:t-622-arti-12-1:gdls.zip|some environment descriptions (one fixed and two with a random initial location)}}
+The file 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 robot is supposed to execute.
+The zip file also contains the description of an example environment (vacuumcleaner.gdl) a simulator (gamecontroller-gui.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).
+  - Setup the simulator as shown in this picture:
+    {{:public:t-622-arti-12-1:gamecontroller-settings.png?nolink&|}}
+  - 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 your agent got: "Game over! results: 0". In the given environment you will only get non-zero points if you manage to clean everything, return to the initial location, and turn off the robot within 100 steps. 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 see [[http://130.208.241.192/ggpserver/public/view_state.jsp?matchID=vacuum_cleaner_1.1326993828477&stepNumber=2&role=RANDOM|here]], what the example environment looks like. Of course, you shouldn't assume any fixed size, initial location or locations of the dirt in your implementation. This is just an example environment.
+[[http://ruclasses.proboards.com/index.cgi?action=gotopost&board=arti2012&thread=103&post=853|Here]] I described how you visualise what your agent is doing.
+===== Hints =====
+For implementing your agent:
+  * Add a new class that implements the "Agent" interface. Look at RandomAgent.java to see how this is done.
+  * You have to implement the method "nextAction" which gets a collection of percepts as input and has to return the next action the agent is supposed to execute.
+  * Before you start programming a complicated strategy, think about it. The things your agent has to do are:
+     - execute TURN_ON
+     - visit every cell and suck up any dirt it finds on the way
+     - return to the initial location
+     - TURN_OFF
+  * For this your agent needs an internal model of the world. Figure out, what you need to remember about the current state of the world. Ideally, you create an object "State" that contains everything you need to remember and update this object depending on which action you executed and which percepts you got. Then you can implement rules that say which action should be executed depending on what properties the current state has.
+As a last and general hint: "Days of programming can save you minutes of thinking." Think of a strategy, the rules to implement it and the information you need to decide on the actions **before** you start implementing it.