Importation du code depuis GitHub

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

+# TDDC17AICourse
+Sharing of my labs carried out during the TDDC17 course at Linköping University (HT 2020).
+
+## Lab 4
+Commands used in lab 4 to use planner algorithms:
+```bash
+$ ipp -o task1_domain.pddl -f task1_pb1.pddl
+$ ff -o task1_domain.pddl -f task1_pb1.pddl
+$ lama task1_domain.pddl task1_pb1.pddl
+```

lab1/myvacuumagent.py

+from lab1.liuvacuum import *
+
+DEBUG_OPT_DENSEWORLDMAP = False
+
+AGENT_STATE_UNKNOWN = 0
+AGENT_STATE_WALL = 1
+AGENT_STATE_CLEAR = 2
+AGENT_STATE_DIRT = 3
+AGENT_STATE_HOME = 4
+
+AGENT_DIRECTION_NORTH = 0
+AGENT_DIRECTION_EAST = 1
+AGENT_DIRECTION_SOUTH = 2
+AGENT_DIRECTION_WEST = 3
+
+def direction_to_string(cdr):
+    cdr %= 4
+    return  "NORTH" if cdr == AGENT_DIRECTION_NORTH else\
+            "EAST"  if cdr == AGENT_DIRECTION_EAST else\
+            "SOUTH" if cdr == AGENT_DIRECTION_SOUTH else\
+            "WEST" #if dir == AGENT_DIRECTION_WEST
+
+"""
+Internal state of a vacuum agent
+"""
+class MyAgentState:
+
+    def __init__(self, width, height):
+
+        # Initialize perceived world state
+        self.world = [[AGENT_STATE_UNKNOWN for _ in range(height)] for _ in range(width)]
+        self.world[1][1] = AGENT_STATE_HOME
+
+        # Agent internal state
+        self.last_action = ACTION_NOP
+        self.direction = AGENT_DIRECTION_EAST
+        self.pos_x = 1
+        self.pos_y = 1
+
+        # Metadata
+        self.world_width = width
+        self.world_height = height
+
+    """
+    Update perceived agent location and orientation
+    """
+    def update_position(self, bump):
+        if not bump and self.last_action == ACTION_FORWARD:
+            if self.direction == AGENT_DIRECTION_EAST:
+                self.pos_x += 1
+            elif self.direction == AGENT_DIRECTION_SOUTH:
+                self.pos_y += 1
+            elif self.direction == AGENT_DIRECTION_WEST:
+                self.pos_x -= 1
+            elif self.direction == AGENT_DIRECTION_NORTH:
+                self.pos_y -= 1
+        elif self.last_action == ACTION_TURN_LEFT:
+            if self.direction == AGENT_DIRECTION_EAST:
+                self.direction = AGENT_DIRECTION_NORTH
+            elif self.direction == AGENT_DIRECTION_SOUTH:
+                self.direction = AGENT_DIRECTION_EAST
+            elif self.direction == AGENT_DIRECTION_WEST:
+                self.direction = AGENT_DIRECTION_SOUTH
+            elif self.direction == AGENT_DIRECTION_NORTH:
+                self.direction = AGENT_DIRECTION_WEST
+        elif self.last_action == ACTION_TURN_RIGHT:
+            if self.direction == AGENT_DIRECTION_EAST:
+                self.direction = AGENT_DIRECTION_SOUTH
+            elif self.direction == AGENT_DIRECTION_SOUTH:
+                self.direction = AGENT_DIRECTION_WEST
+            elif self.direction == AGENT_DIRECTION_WEST:
+                self.direction = AGENT_DIRECTION_NORTH
+            elif self.direction == AGENT_DIRECTION_NORTH:
+                self.direction = AGENT_DIRECTION_EAST
+
+    """
+    Update perceived or inferred information about a part of the world
+    """
+    def update_world(self, x, y, info):
+        self.world[x][y] = info
+
+    """
+    Dumps a map of the world as the agent knows it
+    """
+    def print_world_debug(self):
+        for y in range(self.world_height):
+            for x in range(self.world_width):
+                if self.world[x][y] == AGENT_STATE_UNKNOWN:
+                    print("?" if DEBUG_OPT_DENSEWORLDMAP else " ? ", end="")
+                elif self.world[x][y] == AGENT_STATE_WALL:
+                    print("#" if DEBUG_OPT_DENSEWORLDMAP else " # ", end="")
+                elif self.world[x][y] == AGENT_STATE_CLEAR:
+                    print("." if DEBUG_OPT_DENSEWORLDMAP else " . ", end="")
+                elif self.world[x][y] == AGENT_STATE_DIRT:
+                    print("D" if DEBUG_OPT_DENSEWORLDMAP else " D ", end="")
+                elif self.world[x][y] == AGENT_STATE_HOME:
+                    print("H" if DEBUG_OPT_DENSEWORLDMAP else " H ", end="")
+
+            print() # Newline
+        print() # Delimiter post-print
+
+"""
+Vacuum agent
+"""
+class MyVacuumAgent(Agent):
+    """
+    Init function, everything here is execute once at the beginning
+    """
+    def __init__(self, world_width, world_height, log):
+        super().__init__(self.execute)
+        self.initial_random_actions = 10
+        self.iteration_counter = 1000
+        self.state = MyAgentState(world_width, world_height)
+        self.log = log
+
+        # Arrived variable for the goto function
+        self.arrived = True
+        # Arrived variable for the go_to_with_pathfinder function
+        self.arrived_destination = True
+        # If the agent has finished the cleaning
+        self.finished = False
+
+        # Goto coordinates for the goto function
+        self.goto_x = 1
+        self.goto_y = 1
+
+        # Goto coordinates for the go_to_with_pathfinder function
+        self.destination_x = 1
+        self.destination_y = 1
+
+        # If we need to re-calculate our destination
+        self.reset_destination = True
+
+        # Different list in agent memory
+        self.waypoint = []                  # Memory for next waypoint coordinates
+        self.slist = []                     # List of waypoint to go at the agent destination
+
+        # At the beginning, we assume that the zone is surrounded by wall
+        for i in range(0, world_width):
+            self.state.update_world(i, 0, AGENT_STATE_WALL)
+            self.state.update_world(i, world_height - 1, AGENT_STATE_WALL)
+        for j in range(1, world_height - 1):
+            self.state.update_world(0, j, AGENT_STATE_WALL)
+            self.state.update_world(world_width - 1, j, AGENT_STATE_WALL)
+
+    """
+    Function which move the agent to a ramdom start position
+    """
+    def move_to_random_start_position(self, bump):
+        action = random()
+
+        self.initial_random_actions -= 1
+        self.state.update_position(bump)
+        self.log("Actual direction: " + direction_to_string(self.state.direction))
+
+        if action < 0.1666666:   # 1/6 chance
+            self.state.last_action = ACTION_TURN_LEFT
+            return ACTION_TURN_LEFT
+        elif action < 0.3333333: # 1/6 chance
+            self.state.last_action = ACTION_TURN_RIGHT
+            return ACTION_TURN_RIGHT
+        else:                    # 4/6 chance
+            self.state.last_action = ACTION_FORWARD
+            return ACTION_FORWARD
+
+    """
+    A basic "go to" function which does not consider wall
+    """
+    def goto(self, bump, dirt):
+        self.state.update_position(bump)
+        self.log("Actual direction: " + direction_to_string(self.state.direction))
+        self.log("Next waypoint: {}, {}".format(self.goto_x, self.goto_y))
+
+        if dirt:
+            self.state.update_world(self.state.pos_x, self.state.pos_y, AGENT_STATE_DIRT)
+            self.log("DIRT -> choosing SUCK action!")
+            self.state.last_action = ACTION_SUCK
+            return ACTION_SUCK
+        else:
+            self.state.update_world(self.state.pos_x, self.state.pos_y, AGENT_STATE_CLEAR)
+
+        if (self.goto_x == self.state.pos_x and self.goto_y == self.state.pos_y) or bump:
+            self.arrived = True
+            self.log("Arrived to the target point")
+            self.state.last_action = ACTION_NOP
+            return ACTION_NOP
+
+        if abs(self.state.pos_x - self.goto_x) >= abs(self.state.pos_y - self.goto_y):
+            if self.state.pos_x - self.goto_x > 0:          # Need to go WEST
+                if self.state.direction == AGENT_DIRECTION_WEST:
+                    return ACTION_FORWARD
+                elif self.state.direction == AGENT_DIRECTION_SOUTH:
+                    return ACTION_TURN_RIGHT
+                else:
+                    return ACTION_TURN_LEFT
+            else:                                           # Need to go EAST
+                if self.state.direction == AGENT_DIRECTION_EAST:
+                    return ACTION_FORWARD
+                elif self.state.direction == AGENT_DIRECTION_NORTH:
+                    return ACTION_TURN_RIGHT
+                else:
+                    return ACTION_TURN_LEFT
+        else:
+            if self.state.pos_y - self.goto_y > 0:          # Need to go NORTH
+                if self.state.direction == AGENT_DIRECTION_NORTH:
+                    return ACTION_FORWARD
+                elif self.state.direction == AGENT_DIRECTION_WEST:
+                    return ACTION_TURN_RIGHT
+                else:
+                    return ACTION_TURN_LEFT
+            else:                                           # Need to go SOUTH
+                if self.state.direction == AGENT_DIRECTION_SOUTH:
+                    return ACTION_FORWARD
+                elif self.state.direction == AGENT_DIRECTION_EAST:
+                    return ACTION_TURN_RIGHT
+                else:
+                    return ACTION_TURN_LEFT
+
+    """
+    A pathfinding algorithm, based on the BFS algorithm
+    """
+    def pathfinding(self, target_x, target_y):
+
+        # Class for node used in the search
+        class Node:
+            def __init__(self, parent=None, x=-1, y=-1):
+                self.x = x
+                self.y = y
+                self.parent = parent
+
+            def __eq__(self, other):
+                return self.x == other.x and self.y == other.y
+
+        # Start and end nodes
+        start = Node(None, self.state.pos_x, self.state.pos_y)
+        end = Node(None, target_x, target_y)
+
+        # List containing the nodes to explore (By order of priority)
+        queue = [start]
+        # List of explored nodes
+        explored = []
+        # List which will be returned by the program containing
+        # the coordinates to follow to reach the targeted point
+        slist = []
+
+        # While there is node to explore
+        while queue:
+            # If the queue is to big, we assume that there is a problem
+            if len(queue) > 200:
+                break
+
+            # We take the first node in the queue
+            node = queue.pop(0)
+            # If it is the destination
+            if node == end:
+                print("Trying to go at : {}; {}".format(target_x, target_y))
+                print("Solution found : ")
+                n = node
+                # We return the path found
+                while n.parent:
+                    print("x: {}, y: {}".format(n.x, n.y))
+                    slist.append([n.x, n.y])
+                    n = n.parent
+                # and stop the processing
+                break
+
+            # If we never explored this node
+            elif node not in explored:
+                explored.append(node)
+
+                # For each adjacent node, if it is not a wall,
+                # we add it to the queue
+                if self.state.world[node.x - 1][node.y] != AGENT_STATE_WALL:
+                    queue.append(Node(node, node.x-1, node.y))
+                if self.state.world[node.x + 1][node.y] != AGENT_STATE_WALL:
+                    queue.append(Node(node, node.x+1, node.y))
+                if self.state.world[node.x][node.y - 1] != AGENT_STATE_WALL:
+                    queue.append(Node(node, node.x, node.y - 1))
+                if self.state.world[node.x][node.y + 1] != AGENT_STATE_WALL:
+                    queue.append(Node(node, node.x, node.y+1))
+
+        # If we don't find any solution
+        if not slist:
+            # We consider that the place in inaccessible
+            self.state.update_world(end.x, end.y, AGENT_STATE_WALL)
+            self.log("Find a inaccessible place, marking as a wall ##")
+            print("Overload! Inaccessible place!")
+        return slist
+
+    """
+    A "go to" function which implement the pathfinding algorithm using the goto function
+    """
+    def go_to_with_pathfinder(self, bump, dirt):
+        # If we start or if we need to reset the path
+        if self.reset_destination:
+            print("Launching the pathfinding algorithm.")
+            self.slist = self.pathfinding(self.destination_x, self.destination_y)
+
+        # If we have a path, we follow it
+        if self.slist:
+            # If the agent move correctly the previous time
+            if not bump:
+                self.waypoint = self.slist.pop()
+            self.goto_x = self.waypoint[0]
+            self.goto_y = self.waypoint[1]
+            self.arrived = False
+            action = self.goto(bump, dirt)
+            self.state.last_action = action
+            self.reset_destination = False
+            return action
+        # else (whether we arrived or we don't find a path), we assume
+        # that we will not go further
+        else:
+            self.reset_destination = True
+            self.arrived_destination = True
+            self.state.last_action = ACTION_NOP
+            return ACTION_NOP
+        pass
+
+    """
+    The execute function, which is call at each turn
+    """
+    def execute(self, percept):
+
+        # If the agent has finished, we do nothing
+        if self.finished:
+            return ACTION_NOP
+
+        ###########################
+        # DO NOT MODIFY THIS CODE #
+        ###########################
+
+        bump = percept.attributes["bump"]
+        dirt = percept.attributes["dirt"]
+        home = percept.attributes["home"]
+
+        # Move agent to a randomly chosen initial position
+        if self.initial_random_actions > 0:
+            self.log("Moving to random start position ({} steps left)".format(self.initial_random_actions))
+            return self.move_to_random_start_position(bump)
+
+        # Finalize randomization by properly updating position (without subsequently changing it)
+        elif self.initial_random_actions == 0:
+            self.initial_random_actions -= 1
+            self.state.update_position(bump)
+            self.state.last_action = ACTION_SUCK
+            self.log("Processing percepts after position randomization")
+            return ACTION_SUCK
+
+        ########################
+        # START MODIFYING HERE #
+        ########################
+
+        # Logging position and orientation
+        self.log("Position: ({}, {})\t\tDirection: {}".format(self.state.pos_x, self.state.pos_y,
+                                                              direction_to_string(self.state.direction)))
+
+        if bump:
+            # Not arrived, but this way we force our agent to re-calculate its path
+            self.arrived = True
+            self.reset_destination = True
+            # Get an xy-offset pair based on where the agent is facing
+            offset = [(0, -1), (1, 0), (0, 1), (-1, 0)][self.state.direction]
+
+            # Mark the tile at the offset from the agent as a wall (since the agent bumped into it)
+            self.state.update_world(self.state.pos_x + offset[0], self.state.pos_y + offset[1], AGENT_STATE_WALL)
+
+        # While we aren't arrived, we refer to the goto function
+        if not self.arrived:
+            action = self.goto(bump, dirt)
+            self.state.last_action = action
+            return action
+        # While we aren't arrived, we refer to the go_to_with_pathfinder function
+        elif not self.arrived_destination:
+            return self.go_to_with_pathfinder(bump, dirt)
+
+        # Max iterations for the agent
+        if self.iteration_counter < 1:
+            if self.iteration_counter == 0:
+                self.iteration_counter -= 1
+                self.log("Iteration counter is now 0. Halting!")
+                self.log("Performance: {}".format(self.performance))
+            return ACTION_NOP
+        self.iteration_counter -= 1
+
+        # Track position of agent
+        self.state.update_position(bump)
+
+        # Update perceived state of current tile
+        if dirt:
+            self.state.update_world(self.state.pos_x, self.state.pos_y, AGENT_STATE_DIRT)
+        else:
+            self.state.update_world(self.state.pos_x, self.state.pos_y, AGENT_STATE_CLEAR)
+
+        # Debug
+        self.state.print_world_debug()
+
+        # Variables used to determine the closest unknown place
+        closest_unk_range = self.state.world_height + self.state.world_width + 1
+        closest_x = 1
+        closest_y = 1
+        find_new = False
+
+        # Determination of the nearest unknown location
+        for i in range(self.state.world_width):
+            for j in range(self.state.world_height):
+                if self.state.world[i][j] == AGENT_STATE_UNKNOWN and (
+                        abs(self.state.pos_x - i) + abs(self.state.pos_y - j)) < closest_unk_range:
+                    find_new = True
+                    closest_unk_range = (abs(self.state.pos_x - i) + abs(self.state.pos_y - j))
+                    closest_x = i
+                    closest_y = j
+
+        # If we find a new place to go
+        if find_new:
+            self.log("Going to unknown places : ({};{})".format(closest_x, closest_y))
+            self.destination_x = closest_x
+            self.destination_y = closest_y
+            return self.go_to_with_pathfinder(bump, dirt)
+        # If not, we have finished, we go home
+        if home:
+            self.log("Finished !")
+            self.log("Performance: {}".format(self.performance))
+            self.finished = True
+        # Finally we are at home
+        else:
+            self.log("Job done, going back home.")
+            self.destination_x = 1
+            self.destination_y = 1
+            return self.go_to_with_pathfinder(bump, dirt)
+
+        # Useless
+        self.state.last_action = ACTION_NOP
+        return ACTION_NOP

lab2/CustomBreadthFirstSearch.java

+package searchCustom;
+
+public class CustomBreadthFirstSearch  extends CustomGraphSearch{
+
+	public   CustomBreadthFirstSearch(int maxDepth){
+		super(false);
+		//System.out.println("Change line above in \"CustomBreadthFirstSearch.java\"! --> done.");
+	}
+};

lab2/CustomDepthFirstSearch.java

+package searchCustom;
+
+public class CustomDepthFirstSearch extends CustomGraphSearch{
+	public CustomDepthFirstSearch(int maxDepth){
+		super(true);
+		//System.out.println("Change line above in \"CustomDepthFirstSearch.java\"! --> done.");
+	}
+};

lab2/CustomGraphSearch.java

+package searchCustom;
+
+import java.util.ArrayList;
+import java.util.HashSet;
+
+import searchShared.NodeQueue;
+import searchShared.Problem;
+import searchShared.SearchObject;
+import searchShared.SearchNode;
+
+import world.GridPos;
+
+public class CustomGraphSearch implements SearchObject {
+
+	private HashSet<SearchNode> explored;
+	private NodeQueue frontier;
+	protected ArrayList<SearchNode> path;
+	private boolean insertFront;
+
+	/**
+	 * The constructor tells graph search whether it should insert nodes to front or back of the frontier 
+	 */
+    public CustomGraphSearch(boolean bInsertFront) {
+		insertFront = bInsertFront;
+    }
+
+	/**
+	 * Implements "graph search", which is the foundation of many search algorithms
+	 */
+	public ArrayList<SearchNode> search(Problem problem) {
+		// The frontier is a queue of expanded SearchNodes not processed yet
+		frontier = new NodeQueue();
+		// The explored set is a set of nodes that have been processed 
+		explored = new HashSet<SearchNode>();
+		// The start state is given
+		GridPos startState = (GridPos) problem.getInitialState();
+		// Initialise the frontier with the start state  
+		frontier.addNodeToFront(new SearchNode(startState));
+
+		// Path will be empty until we find the goal.
+		path = new ArrayList<SearchNode>();
+		
+		
+		/*
+		 * Note on how java and this program work:
+		 *  -ArrayList are just resizable array. "<>" contains the type of the listed objects
+		 *  -Structures :
+		 *  	while (boolean) {}
+		 *  	while (a > 0) {}
+		 *  	for (int i = 0; i < 5; i++){}		--> 5 iterations
+		 *  	for (String i : StringList){}		--> Iterations on all the objects in the list
+		 *  -Class used here:
+		 *  	-SearchNode					--> Equivalent of the "Node" class in the Python program. Represent a node.
+		 *  	-Problem 					--> Class containing lot of informations on the problem
+		 *  									like the reachable states from a position, the start, the end
+		 *  	-GridPos 					--> represent a position (with x and y coordinates)  	
+		 */
+		
+		
+		// While we have something to explore
+		while (frontier.size() > 0) {
+			// Get the first node on the list
+			SearchNode Node = frontier.removeFirst();
+			
+			// We check if it is our destination
+			if (problem.isGoalState(Node.getState())) {
+				return path = Node.getPathFromRoot();
+			} // if not, we continue
+			
+			// Get the list of children of the current node
+			ArrayList<GridPos> childPosition = problem.getReachableStatesFrom(Node.getState());
+			// For every child in the list
+			for (GridPos position : childPosition) {
+				// Creating a new node
+				SearchNode childNode = new SearchNode(position, Node);
+				// If we have not explored the node
+				if (explored.contains(childNode) == false) {
+					// Adding the child node to the frontier
+					// Front --> DFS (LIFO queue)
+					// Back --> BFS (FIFO queue)
+					if (insertFront) {
+						frontier.addNodeToFront(childNode);
+					}
+					else {
+						frontier.addNodeToBack(childNode);
+					}
+					// We add the node to explored node
+					explored.add(childNode);
+				}
+			}
+		}
+		
+		
+		/* Some hints:
+		 * -Read early part of chapter 3 in the book!
+		 * -You are free to change anything how you wish as long as the program runs, but some structure is given to help you.
+		 * -You can Google for "javadoc <class>" if you are uncertain of what you can do with a particular Java type.
+		 * 
+		 * -SearchNodes are the nodes of the search tree and contains the relevant problem state, in this case x,y position (GridPos) of the agent 
+		 * --You can create a new search node from a state by: SearchNode childNode = new SearchNode(childState, currentNode);
+		 * --You can also extract the state by .getState() method
+		 * --All search structures use search nodes, but the problem object only speaks in state, so you may need to convert between them 
+		 * 
+		 * -The frontier is a queue of search nodes, open this class to find out what you can do with it! 
+		 * 
+		 * -If you are unfamiliar with Java, the "HashSet<SearchNode>" used for the explored set means a set of SearchNode objects.
+		 * --You can add nodes to the explored set, or check if it contains a node!
+		 * 
+		 * -To get the child states (adjacent grid positions that are not walls) of a particular search node, do: ArrayList<GridPos> childStates = p.getReachableStatesFrom(currentState);
+		 * 
+		 * -Depending on the addNodesToFront boolean variable, you may need to do something with the frontier... (see book)
+		 * 
+		 * -You can check if you have reached the goal with p.isGoalState(NodeState)
+		 * 
+		 *  When the goal is found, the path to be returned can be found by: path = node.getPathFromRoot();
+		 */
+		/* Note: Returning an empty path signals that no path exists */
+		return path;
+	}
+
+	/*
+	 * Functions below are just getters used externally by the program 
+	 */
+	public ArrayList<SearchNode> getPath() {
+		return path;
+	}
+
+	public ArrayList<SearchNode> getFrontierNodes() {
+		return new ArrayList<SearchNode>(frontier.toList());
+	}
+	public ArrayList<SearchNode> getExploredNodes() {
+		return new ArrayList<SearchNode>(explored);
+	}
+	public ArrayList<SearchNode> getAllExpandedNodes() {
+		ArrayList<SearchNode> allNodes = new ArrayList<SearchNode>();
+		allNodes.addAll(getFrontierNodes());
+		allNodes.addAll(getExploredNodes());
+		return allNodes;
+	}
+
+}

lab3/bayes.jnlp

+<?xml version="1.0" encoding="UTF-8"?>
+<jnlp spec="1.0+"
+  codebase="http://www.aispace.org/"
+>
+<information>
+  <title>Belief and Decision Networks</title>
+  <vendor>AIspace</vendor>
+  <homepage href="www.aispace.org" />
+</information>
+<offline-allowed/>
+<security>
+  <all-permissions/>
+</security>
+<resources>
+  <j2se version="1.5+" />
+  <jar href="bayes/bayes.jar"/>
+</resources>
+<application-desc />
+</jnlp>
+

lab3/lab3_Mr_HS_v2.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<BIF VERSION="0.3"  xmlns="http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3"
+	xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+	xsi:schemaLocation="http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3 http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3/XMLBIFv0_3.xsd">
+<NETWORK>
+<NAME>Nuclear Power Station</NAME>
+<PROPERTY>detailed = </PROPERTY>
+<PROPERTY>short = </PROPERTY>
+
+<VARIABLE TYPE="nature">
+	<NAME>WaterLeak</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7367.91845703125, 5357.0380859375)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>WaterLeakWarning</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7195.8505859375, 5497.32470703125)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>IcyWeather</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7565.81103515625, 5097.51025390625)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>PumpFailure</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7121.32080078125, 5349.66455078125)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>PumpFailureWarning</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (6830.72802734375, 5504.23828125)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Meltdown</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7467.087890625, 5603.3173828125)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Battery</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7761.697265625, 5233.7236328125)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Radio</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7667.05322265625, 5364.93359375)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Ignition</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7914.4169921875, 5364.93359375)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Gas</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (8158.5546875, 5373.53759765625)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Start</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (8051.0048828125, 5523.03076171875)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Moves</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (8051.0048828125, 5678.9775390625)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Survives</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7638.01513671875, 5822.0185546875)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Bicycle_works</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7636.93896484375, 5681.12890625)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>HS_is_awake</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<OBS>F</OBS>
+	<PROPERTY>position = (7004.9658203125, 5804.48974609375)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>HS_do_something_not_stupid</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7317.30908203125, 5806.892578125)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>HS_remark_irregularity</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7008.78662109375, 5680.53564453125)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Pay_day</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (6790.44482421875, 5682.78271484375)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Warning(s)?</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<OBS>T</OBS>
+	<PROPERTY>position = (7014.93408203125, 5581.3759765625)</PROPERTY>
+</VARIABLE>
+
+<DEFINITION>
+	<FOR>WaterLeak</FOR>
+	<GIVEN>IcyWeather</GIVEN>
+	<TABLE>0.2 0.8 0.1 0.9</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>WaterLeakWarning</FOR>
+	<GIVEN>WaterLeak</GIVEN>
+	<TABLE>0.9 0.1 0.05 0.95</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>IcyWeather</FOR>
+	<TABLE>0.05 0.95</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>PumpFailure</FOR>
+	<TABLE>0.1 0.9</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>PumpFailureWarning</FOR>
+	<GIVEN>PumpFailure</GIVEN>
+	<TABLE>0.9 0.1 0.05 0.95</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Meltdown</FOR>
+	<GIVEN>WaterLeak</GIVEN>
+	<GIVEN>PumpFailure</GIVEN>
+	<GIVEN>HS_do_something_not_stupid</GIVEN>
+	<TABLE>0.15 0.85 0.2 0.8 0.05 0.95 0.1 0.9 0.1 0.9 0.15 0.85 0.001 0.999 0.001 0.999</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Battery</FOR>
+	<GIVEN>IcyWeather</GIVEN>
+	<TABLE>0.8 0.2 0.95 0.05</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Radio</FOR>
+	<GIVEN>Battery</GIVEN>
+	<TABLE>0.95 0.05 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Ignition</FOR>
+	<GIVEN>Battery</GIVEN>
+	<TABLE>0.95 0.05 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Gas</FOR>
+	<TABLE>0.95 0.05</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Start</FOR>
+	<GIVEN>Ignition</GIVEN>
+	<GIVEN>Gas</GIVEN>
+	<TABLE>0.95 0.05 0.0 1.0 0.0 1.0 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Moves</FOR>
+	<GIVEN>Start</GIVEN>
+	<TABLE>0.95 0.05 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Survives</FOR>
+	<GIVEN>Meltdown</GIVEN>
+	<GIVEN>Moves</GIVEN>
+	<GIVEN>Bicycle_works</GIVEN>
+	<TABLE>0.9 0.1 0.8 0.2 0.6 0.4 0.0 1.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Bicycle_works</FOR>
+	<TABLE>0.9 0.1</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>HS_is_awake</FOR>
+	<GIVEN>Pay_day</GIVEN>
+	<TABLE>1.0 0.0 0.5 0.5</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>HS_do_something_not_stupid</FOR>
+	<GIVEN>HS_is_awake</GIVEN>
+	<GIVEN>HS_remark_irregularity</GIVEN>
+	<TABLE>0.7 0.3 0.05 0.95 0.0 1.0 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>HS_remark_irregularity</FOR>
+	<GIVEN>WaterLeakWarning</GIVEN>
+	<GIVEN>PumpFailureWarning</GIVEN>
+	<GIVEN>HS_is_awake</GIVEN>
+	<TABLE>0.9 0.1 0.0 1.0 0.7 0.3 0.0 1.0 0.7 0.3 0.0 1.0 0.05 0.95 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Pay_day</FOR>
+	<TABLE>0.03 0.97</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Warning(s)?</FOR>
+	<GIVEN>WaterLeakWarning</GIVEN>
+	<GIVEN>PumpFailureWarning</GIVEN>
+	<TABLE>1.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0</TABLE>
+</DEFINITION>
+</NETWORK>
+</BIF>

lab3/lab3_bicycle.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<BIF VERSION="0.3"  xmlns="http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3"
+	xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+	xsi:schemaLocation="http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3 http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3/XMLBIFv0_3.xsd">
+<NETWORK>
+<NAME>Nuclear Power Station</NAME>
+<PROPERTY>detailed = </PROPERTY>
+<PROPERTY>short = </PROPERTY>
+
+<VARIABLE TYPE="nature">
+	<NAME>WaterLeak</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7384.1337890625, 5342.26220703125)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>WaterLeakWarning</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7260.0986328125, 5584.7626953125)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>IcyWeather</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7579.63818359375, 5085.86474609375)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>PumpFailure</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7123.5244140625, 5338.97509765625)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>PumpFailureWarning</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7029.26171875, 5583.60107421875)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Meltdown</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7482.10693359375, 5585.57177734375)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Battery</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7773.16650390625, 5220.43408203125)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Radio</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7679.6611328125, 5350.0634765625)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Ignition</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7924.04248046875, 5350.0634765625)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Gas</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (8165.234375, 5358.56396484375)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Start</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (8058.9833984375, 5506.25390625)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Moves</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (8058.9833984375, 5660.31982421875)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Survives</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7650.9716796875, 5801.63623046875)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Bicycle_works</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7649.91162109375, 5662.4453125)</PROPERTY>
+</VARIABLE>
+
+<DEFINITION>
+	<FOR>WaterLeak</FOR>
+	<GIVEN>IcyWeather</GIVEN>
+	<TABLE>0.2 0.8 0.1 0.9</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>WaterLeakWarning</FOR>
+	<GIVEN>WaterLeak</GIVEN>
+	<TABLE>0.9 0.1 0.05 0.95</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>IcyWeather</FOR>
+	<TABLE>0.05 0.95</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>PumpFailure</FOR>
+	<TABLE>0.1 0.9</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>PumpFailureWarning</FOR>
+	<GIVEN>PumpFailure</GIVEN>
+	<TABLE>0.9 0.1 0.05 0.95</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Meltdown</FOR>
+	<GIVEN>WaterLeak</GIVEN>
+	<GIVEN>PumpFailure</GIVEN>
+	<TABLE>0.2 0.8 0.1 0.9 0.15 0.85 0.001 0.999</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Battery</FOR>
+	<GIVEN>IcyWeather</GIVEN>
+	<TABLE>0.8 0.2 0.95 0.05</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Radio</FOR>
+	<GIVEN>Battery</GIVEN>
+	<TABLE>0.95 0.05 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Ignition</FOR>
+	<GIVEN>Battery</GIVEN>
+	<TABLE>0.95 0.05 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Gas</FOR>
+	<TABLE>0.95 0.05</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Start</FOR>
+	<GIVEN>Ignition</GIVEN>
+	<GIVEN>Gas</GIVEN>
+	<TABLE>0.95 0.05 0.0 1.0 0.0 1.0 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Moves</FOR>
+	<GIVEN>Start</GIVEN>
+	<TABLE>0.95 0.05 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Survives</FOR>
+	<GIVEN>Meltdown</GIVEN>
+	<GIVEN>Moves</GIVEN>
+	<GIVEN>Bicycle_works</GIVEN>
+	<TABLE>0.9 0.1 0.8 0.2 0.6 0.4 0.0 1.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Bicycle_works</FOR>
+	<TABLE>0.9 0.1</TABLE>
+</DEFINITION>
+</NETWORK>
+</BIF>

lab3/lab3_new_car.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<BIF VERSION="0.3"  xmlns="http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3"
+	xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+	xsi:schemaLocation="http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3 http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3/XMLBIFv0_3.xsd">
+<NETWORK>
+<NAME>Nuclear Power Station</NAME>
+<PROPERTY>detailed = </PROPERTY>
+<PROPERTY>short = </PROPERTY>
+
+<VARIABLE TYPE="nature">
+	<NAME>WaterLeak</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7384.1337890625, 5342.26220703125)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>WaterLeakWarning</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7260.0986328125, 5584.7626953125)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>IcyWeather</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7579.63818359375, 5085.86474609375)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>PumpFailure</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7123.5244140625, 5338.97509765625)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>PumpFailureWarning</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7029.26171875, 5583.60107421875)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Meltdown</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7482.10693359375, 5585.57177734375)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Battery</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7773.16650390625, 5220.43408203125)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Radio</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7679.6611328125, 5350.0634765625)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Ignition</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7924.04248046875, 5350.0634765625)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Gas</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (8165.234375, 5358.56396484375)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Start</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (8058.9833984375, 5506.25390625)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Moves</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (8058.9833984375, 5660.31982421875)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Survives</NAME>
+	<OUTCOME>T</OUTCOME>
+	<OUTCOME>F</OUTCOME>
+	<PROPERTY>position = (7650.9716796875, 5801.63623046875)</PROPERTY>
+</VARIABLE>
+
+<DEFINITION>
+	<FOR>WaterLeak</FOR>
+	<GIVEN>IcyWeather</GIVEN>
+	<TABLE>0.2 0.8 0.1 0.9</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>WaterLeakWarning</FOR>
+	<GIVEN>WaterLeak</GIVEN>
+	<TABLE>0.9 0.1 0.05 0.95</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>IcyWeather</FOR>
+	<TABLE>0.05 0.95</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>PumpFailure</FOR>
+	<TABLE>0.1 0.9</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>PumpFailureWarning</FOR>
+	<GIVEN>PumpFailure</GIVEN>
+	<TABLE>0.9 0.1 0.05 0.95</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Meltdown</FOR>
+	<GIVEN>WaterLeak</GIVEN>
+	<GIVEN>PumpFailure</GIVEN>
+	<TABLE>0.2 0.8 0.1 0.9 0.15 0.85 0.001 0.999</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Battery</FOR>
+	<GIVEN>IcyWeather</GIVEN>
+	<TABLE>0.8 0.2 0.95 0.05</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Radio</FOR>
+	<GIVEN>Battery</GIVEN>
+	<TABLE>0.95 0.05 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Ignition</FOR>
+	<GIVEN>Battery</GIVEN>
+	<TABLE>0.95 0.05 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Gas</FOR>
+	<TABLE>0.95 0.05</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Start</FOR>
+	<GIVEN>Ignition</GIVEN>
+	<GIVEN>Gas</GIVEN>
+	<TABLE>0.95 0.05 0.0 1.0 0.0 1.0 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Moves</FOR>
+	<GIVEN>Start</GIVEN>
+	<TABLE>0.95 0.05 0.0 1.0</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+	<FOR>Survives</FOR>
+	<GIVEN>Meltdown</GIVEN>
+	<GIVEN>Moves</GIVEN>
+	<TABLE>0.8 0.2 0.0 1.0 1.0 0.0 1.0 0.0</TABLE>
+</DEFINITION>
+</NETWORK>
+</BIF>

lab3/nuclear_power_station.xml

+<?xml version="1.0" encoding="US-ASCII"?>
+
+<BIF VERSION="0.3"  xmlns="http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3"
+	xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+	xsi:schemaLocation="http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3 http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3/XMLBIFv0_3.xsd">
+<NETWORK>
+<NAME>Nuclear Power Station</NAME>
+<VARIABLE TYPE="nature">
+   <NAME>WaterLeak</NAME>
+   <OUTCOME>T</OUTCOME>
+   <OUTCOME>F</OUTCOME>
+   <PROPERTY>position = (53.0, -52.0)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+   <NAME>WaterLeakWarning</NAME>
+   <OUTCOME>T</OUTCOME>
+   <OUTCOME>F</OUTCOME>
+   <PROPERTY>position = (112.0, 49.0)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+   <NAME>IcyWeather</NAME>
+   <OUTCOME>T</OUTCOME>
+   <OUTCOME>F</OUTCOME>
+   <PROPERTY>position = (53.0, -144.0)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+   <NAME>PumpFailure</NAME>
+   <OUTCOME>T</OUTCOME>
+   <OUTCOME>F</OUTCOME>
+   <PROPERTY>position = (-145.0, -54.0)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+   <NAME>PumpFailureWarning</NAME>
+   <OUTCOME>T</OUTCOME>
+   <OUTCOME>F</OUTCOME>
+   <PROPERTY>position = (-203.0, 47.0)</PROPERTY>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+   <NAME>Meltdown</NAME>
+   <OUTCOME>T</OUTCOME>
+   <OUTCOME>F</OUTCOME>
+   <PROPERTY>position = (-60.0, 140.0)</PROPERTY>
+</VARIABLE>
+
+
+<DEFINITION>
+   <FOR>WaterLeak</FOR>
+   <GIVEN>IcyWeather</GIVEN>
+   <TABLE> 0.2 0.8 0.1 0.9</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+   <FOR>WaterLeakWarning</FOR>
+   <GIVEN>WaterLeak</GIVEN>
+   <TABLE> 0.9 0.1 0.05 0.95</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+   <FOR>IcyWeather</FOR>
+   <TABLE> 0.05 0.95</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+   <FOR>PumpFailure</FOR>
+   <TABLE> 0.1 0.9</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+   <FOR>PumpFailureWarning</FOR>
+   <GIVEN>PumpFailure</GIVEN>
+   <TABLE> 0.9 0.1 0.05 0.95</TABLE>
+</DEFINITION>
+
+<DEFINITION>
+   <FOR>Meltdown</FOR>
+   <GIVEN>PumpFailure</GIVEN>
+   <GIVEN>WaterLeak</GIVEN>
+   <TABLE> 0.2 0.8 0.15 0.85 0.1 0.9 0.0010 0.999</TABLE>
+</DEFINITION>
+
+
+</NETWORK>
+</BIF>

lab4/part1/results.md

+## Some results for pb1 on domain "Shakey"
+The problem 1 is not complex enough to have time comparison, but we can observe different strategies.
+
+### IPP algorithm
+```
+time step    0: MOVE R2 R1
+time step    1: PUSH BOX1 R1 R2
+time step    2: PUSH BOX1 R2 R3
+time step    3: TURN_ON_LIGHT R3 BOX1
+time step    4: GRAB SMALL4 R3 LEFT
+                GRAB SMALL3 R3 RIGHT
+time step    5: PUSH BOX1 R3 R2
+time step    6: PUSH BOX1 R2 R1
+time step    7: RELEASE SMALL3 R1 RIGHT
+                RELEASE SMALL4 R1 LEFT
+time step    8: PUSH BOX1 R1 R2
+time step    9: TURN_ON_LIGHT R2 BOX1
+time step   10: GRAB SMALL2 R2 RIGHT
+time step   11: PUSH BOX1 R2 R1
+time step   12: RELEASE SMALL2 R1 RIGHT
+
+Memory used:   0.09 MBytes for domain representation
+               2.50 MBytes for graph
+               0.04 MBytes for exclusions
+               0.04 MBytes for memoization
+               0.14 MBytes for wave front
+```
+### FF algorithm
+```
+step    0: MOVE R2 R1
+        1: PUSH BOX1 R1 R2
+        2: TURN_ON_LIGHT R2 BOX1
+        3: GRAB SMALL2 R2 LEFT
+        4: MOVE R2 R1
+        5: RELEASE SMALL2 R1 LEFT
+        6: MOVE R1 R2
+        7: PUSH BOX1 R2 R3
+        8: TURN_ON_LIGHT R3 BOX1
+        9: GRAB SMALL3 R3 LEFT
+       10: GRAB SMALL4 R3 RIGHT
+       11: MOVE R3 R2
+       12: MOVE R2 R1
+       13: RELEASE SMALL3 R1 LEFT
+       14: RELEASE SMALL4 R1 RIGHT
+```
+### LAMA algorithm
+```
+(move r2 r1)
+(push box1 r1 r2)
+(turn_on_light r2 box1)
+(grab small2 r2 right)
+(move r2 r1)
+(release small2 r1 right)
+(move r1 r2)
+(push box1 r2 r3)
+(turn_on_light r3 box1)
+(grab small3 r3 left)
+(grab small4 r3 right)
+(move r3 r2)
+(move r2 r1)
+(release small3 r1 left)
+(release small4 r1 right)
+```

lab4/part1/task1_domain.pddl

+;; TDDC17 - Lab 4
+;; Shakey's World
+
+;; The objective here is to give a basic
+;; domain for the Shakey's World problem.
+
+;; Our domain definition
+(define (domain shakey)
+	;; We only use strips
+	(:requirements :strips)	
+	
+	(:types 
+		room	;; Represent a room
+		object	;; Represent any object (small objects or boxes)
+		gripper ;; Represent Shakey's gripper
+	)	
+
+	;; Predicates definition
+	(:predicates
+		(position ?r - room)			;; Is Shakey in the given room?
+		(light ?r - room)			;; Are lights on in the given room?
+
+		(connected_wide ?r1 ?r2 - room)		;; Are rooms connected by a wide door?
+		(connected ?r1 ?r2 - room)		;; Are rooms connected by a door?
+		
+		(in ?o - object ?r - room)		;; Is the given object in the given room?
+		(is_empty ?g - gripper)			;; Is the given gripper empty?
+		(hold_on ?g - gripper ?o - object)	;; Is the given gripper holding the given object?
+		(is_big ?o - object)			;; Is this object big? I.e. is this object a box?
+	)
+
+	;; Actions definition
+	;; Robot's actions
+	(:action move		;; MOVE action, from the room ?pos to ?trg
+		:parameters	(?pos ?trg - room)
+		:precondition	(and (position ?pos) 
+				(connected ?pos ?trg))
+		:effect		(and (not (position ?pos)) (position ?trg))
+	)
+	(:action turn_on_light	;; Act of turning on lights. Requires a support.
+		:parameters	(?pos - room ?support - object)
+		:precondition   (and (position ?pos)
+				     (in ?support ?pos)
+				     (is_big ?support)
+				     (not (light ?pos)))
+		:effect		(light ?pos)
+	)
+
+	;; Object actions
+	(:action grab		;; Action to GRAB sth
+		:parameters	(?what - object ?where - room ?g - gripper)
+		:precondition	(and (position ?where)
+				     (light ?where)
+				     (in ?what ?where)
+				     (is_empty ?g)
+				     (not (is_big ?what)))
+		:effect		(and (not (is_empty ?g))(hold_on ?g ?what)(in ?what ?where))
+	)
+	(:action release	;; Action to RELEASE sth
+		:parameters	(?what - object ?where - room ?g - gripper)
+		:precondition   (and (position ?where)
+				     (hold_on ?g ?what))
+		:effect		(and (not (hold_on ?g ?what))(in ?what ?where)(is_empty ?g))
+	)
+	(:action push		;; Action to PUSH sth
+		:parameters	(?what - object ?r1 - room ?r2 - room)
+		:precondition   (and (position ?r1)
+				     (is_big ?what)
+				     (in ?what ?r1)
+				     (connected_wide ?r1 ?r2))
+		:effect		(and (position ?r2)(not (position ?r1))(in ?what ?r2)(not(in ?what ?r1)))
+	)
+)	

lab4/part1/task1_pb1.pddl

+;; TDDC17 - Lab 4
+;; S.'s W. problem 1
+;; Last modification: 2020-10-04
+
+;; Problem description
+;; The first problem has three rooms, connect by doors as discribed below:
+;;      -------------------------------------------------------------------------
+;;      |                       |                       |                       |
+;;      |                       |                       |                       |
+;;      |       light switch 1 -|- light switch2        |- light switch3        |
+;;      |                       |                       |                       |
+;;      |       ---             |                     door2                     |
+;;      |       | |           door1      shakey         |                       |
+;;      |       ---           (wide)                    |                       |
+;;      |       box             |                       |                       |
+;;      |                       |                     door3                     |
+;;      |                       |                     (wide)                    |
+;;      |        r1             |        r2             |         r3            |
+;;      -------------------------------------------------------------------------
+;; Shakey must find four small objects distributed in the three rooms and bring 
+;; them back to the first room. All lights are off initially. The box allowing 
+;; Shakey to turn on the lights is in the first room.
+
+
+;; Our problem1 definition
+(define (problem pb1)
+	(:domain shakey)
+	
+	(:objects
+		;; Our three rooms
+		r1 - room
+		r2 - room
+		r3 - room
+
+		;; The box
+		box1 - object
+
+		;; The four small objects
+		small1 - object
+		small2 - object
+		small3 - object
+		small4 - object
+
+		;; The two Shakey's grippers
+		right - gripper
+		left - gripper
+	)
+	
+	(:init
+		;; Init position
+		;; At the beginning, Shakey is in room 2.
+		(position r2)
+		
+		;; Init connections (as discribed above).
+		(connected_wide r1 r2)
+		(connected_wide r2 r1)
+		(connected_wide r2 r3)
+		(connected_wide r3 r2)
+		(connected r1 r2)
+		(connected r2 r1)
+		(connected r2 r3)
+		(connected r3 r2)
+
+		;; Init object positions
+		(in box1 r1)
+		(is_big box1)
+		(in small1 r1)
+		(in small2 r2)
+		(in small3 r3)
+		(in small4 r3)
+		
+		;; Init light (light nowhere)
+		;;(not (light r1))
+		;;(not (light r2))
+		;;(not (light r3))
+
+		;; Init gripper
+		(is_empty right)
+		(is_empty left)
+	)
+	
+	;; The goal is to have all four objects in the room 1.
+	(:goal
+		(and (in small1 r1)
+		(in small2 r1)
+                (in small3 r1)
+                (in small4 r1))
+	)
+)

lab4/part2/domain.pddl

+(define (domain transport-strips)
+(:requirements :typing :action-costs)
+
+(:types location fuellevel locatable - object 
+	package truck - locatable
+)
+
+(:predicates 
+(connected ?l1 ?l2 - location)
+(at ?o - locatable ?l - location)
+(in ?p - package ?t - truck)
+(fuel ?t - truck ?level - fuellevel)
+(fuelcost ?level - fuellevel ?l1 ?l2 - location)
+(sum ?a ?b ?c - fuellevel)
+)
+
+(:functions 
+(total-cost) - number)
+
+(:action LOAD
+:parameters
+(?p - package
+?t - truck
+?l - location)
+:precondition
+(and (at ?t ?l) (at ?p ?l))
+:effect
+(and (not (at ?p ?l)) (in ?p ?t) (increase (total-cost) 1))
+)
+
+(:action UNLOAD
+:parameters
+(?p - package
+?t - truck
+?l - location)
+:precondition
+(and (at ?t ?l) (in ?p ?t))
+:effect
+(and (at ?p ?l) (not (in ?p ?t)) (increase (total-cost) 1))
+)
+
+(:action DRIVE
+:parameters
+(?t - truck
+?l1 - location
+?l2 - location
+?fuelpost - fuellevel
+?fueldelta - fuellevel
+?fuelpre - fuellevel)
+:precondition
+(and 
+(connected ?l1 ?l2)
+(fuelcost ?fueldelta ?l1 ?l2)
+(fuel ?t ?fuelpre)
+(sum ?fuelpost ?fueldelta ?fuelpre)
+(at ?t ?l1)
+)
+:effect
+(and (not (at ?t ?l1)) 
+     (at ?t ?l2) 
+     (not (fuel ?t ?fuelpre)) 
+     (fuel ?t ?fuelpost)
+     (increase (total-cost) 1))
+)
+
+)
+
+

lab4/part2/exe.sh

+#!/bin/bash
+# TDDC17 
+# Script to run all problems with all configurations
+
+echo "Setting up env."
+mkdir output
+echo -ne "Working in: "
+pwd
+echo ""
+
+## CONFIG 1: FF heuristic
+echo "$i: Running configuration 1 (Eager greedy search, FF heuristic)"
+/courses/TDDC17/sw/fdlog/fast-downward.py domain.pddl p02.pddl --log-file output/output-config1FF-pb2 --heuristic 'hff=ff()' --search 'eager_greedy([hff])'
+/courses/TDDC17/sw/fdlog/fast-downward.py domain.pddl p03.pddl --log-file output/output-config1FF-pb3 --heuristic 'hff=ff()' --search 'eager_greedy([hff])'
+
+## CONFIG 2: Goal count heristic
+echo "$i: Running configuration 2 (Eager greedy search, Goal count heuristic)"
+/courses/TDDC17/sw/fdlog/fast-downward.py domain.pddl p02.pddl --log-file output/output-config2GC-pb2 --heuristic 'gc=goalcount()' --search 'eager_greedy([gc])'
+/courses/TDDC17/sw/fdlog/fast-downward.py domain.pddl p03.pddl --log-file output/output-config2GC-pb3 --heuristic 'gc=goalcount()' --search 'eager_greedy([gc])'
+
+## CONFIG 3: Goal count heuristic with FF helpful actions
+echo "$i: Running configuration 2 (Eager greedy search, goal count heuristic with FF helpful actions)"
+/courses/TDDC17/sw/fdlog/fast-downward.py domain.pddl p02.pddl --log-file output/output-config3GF-pb2 --heuristic 'gc=goalcount()' --heuristic 'hff=ff()' --search 'eager_greedy([gc], preferred=[hff], boost=256)'
+/courses/TDDC17/sw/fdlog/fast-downward.py domain.pddl p03.pddl --log-file output/output-config3GF-pb3 --heuristic 'gc=goalcount()' --heuristic 'hff=ff()' --search 'eager_greedy([gc], preferred=[hff], boost=256)'
+
+echo "Done."
+exit 0

lab4/part2/output_shakey/output-config1ff-shakey.txt

+{"act": "begin_group", "ts": 0}
+{"act": "open_list_sorting", "key_list": ["h_SINGLE", "h_(", "h_ff", "h_INSERTION_ORDER", "h_)"]}
+{"ts":0, "act": "state_update", "state_id": 0, "state": {"var0": "0(Atom in(box1, r1))", "var1": "1(NegatedAtom in(box1, r3))", "var2": "1(Atom position(r2))", "var3": "1(NegatedAtom in(box1, r2))", "var4": "1(NegatedAtom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "1(NegatedAtom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "4(Atom is_empty(left))", "var13": "4(Atom is_empty(right))", "var14": "1(NegatedAtom in(small2, r1))", "var15": "1(NegatedAtom in(small3, r1))", "var16": "1(NegatedAtom in(small4, r1))"}, "g": -1, "real_g": -1}
+{"ts": 0, "act": "evaluated_states", "state_id": 0, "preferred": 1}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 0}
+{"ts":0, "act": "state_update", "state_id": 0, "h_ff": 12}
+{"ts": 0, "act": "evaluated_heuristics", "count": 1}
+{"act": "preferred_heuristics", "key_list": []}
+{"act": "main_heuristic", "target": "h_ff"}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 1}
+{"ts":1, "act": "state_update", "state_id": 0, "g": 0, "real_g": 0}
+{"ts": 1, "act": "expanded_states", "state_id": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 1}
+{"ts":1, "act": "state_update", "state_id": 1, "state": {"var0": "0(Atom in(box1, r1))", "var1": "1(NegatedAtom in(box1, r3))", "var2": "0(Atom position(r1))", "var3": "1(NegatedAtom in(box1, r2))", "var4": "1(NegatedAtom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "1(NegatedAtom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "4(Atom is_empty(left))", "var13": "4(Atom is_empty(right))", "var14": "1(NegatedAtom in(small2, r1))", "var15": "1(NegatedAtom in(small3, r1))", "var16": "1(NegatedAtom in(small4, r1))"}, "new_parent": [0, "move r2 r1", 1]}
+{"ts": 1, "act": "generated_states", "state_id": 0, "op": "move r2 r1"}
+{"ts":1, "act": "state_update", "state_id": 1, "g": -1, "real_g": -1}
+{"ts": 1, "act": "evaluated_states", "state_id": 1, "preferred": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 1}
+{"ts":1, "act": "state_update", "state_id": 1, "h_ff": 12}
+{"ts": 1, "act": "evaluated_heuristics", "count": 1}
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+{"ts":17, "act": "state_update", "state_id": 54, "state": {"var0": "1(NegatedAtom in(box1, r1))", "var1": "1(NegatedAtom in(box1, r3))", "var2": "1(Atom position(r2))", "var3": "0(Atom in(box1, r2))", "var4": "0(Atom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "0(Atom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "4(Atom is_empty(left))", "var13": "4(Atom is_empty(right))", "var14": "1(NegatedAtom in(small2, r1))", "var15": "0(Atom in(small3, r1))", "var16": "0(Atom in(small4, r1))"}, "new_parent": [53, "move r1 r2", 1]}
+{"ts": 17, "act": "generated_states", "state_id": 53, "op": "move r1 r2"}
+{"ts":17, "act": "state_update", "state_id": 54, "g": -1, "real_g": -1}
+{"ts": 17, "act": "evaluated_states", "state_id": 54, "preferred": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 17}
+{"ts":17, "act": "state_update", "state_id": 54, "h_ff": 3}
+{"ts": 17, "act": "evaluated_heuristics", "count": 1}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 18}
+{"ts":18, "act": "state_update", "state_id": 54, "g": 12, "real_g": 12}
+{"ts": 18, "act": "expanded_states", "state_id": 54}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 18}
+{"ts":18, "act": "state_update", "state_id": 53, "new_parent": [54, "move r2 r1", 1]}
+{"ts": 18, "act": "generated_states", "state_id": 54, "op": "move r2 r1"}
+{"ts":18, "act": "state_update", "state_id": 55, "state": {"var0": "1(NegatedAtom in(box1, r1))", "var1": "1(NegatedAtom in(box1, r3))", "var2": "2(Atom position(r3))", "var3": "0(Atom in(box1, r2))", "var4": "0(Atom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "0(Atom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "4(Atom is_empty(left))", "var13": "4(Atom is_empty(right))", "var14": "1(NegatedAtom in(small2, r1))", "var15": "0(Atom in(small3, r1))", "var16": "0(Atom in(small4, r1))"}, "new_parent": [54, "move r2 r3", 1]}
+{"ts": 18, "act": "generated_states", "state_id": 54, "op": "move r2 r3"}
+{"ts":18, "act": "state_update", "state_id": 55, "g": -1, "real_g": -1}
+{"ts": 18, "act": "evaluated_states", "state_id": 55, "preferred": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 18}
+{"ts":18, "act": "state_update", "state_id": 55, "h_ff": 4}
+{"ts": 18, "act": "evaluated_heuristics", "count": 1}
+{"ts":18, "act": "state_update", "state_id": 56, "state": {"var0": "0(Atom in(box1, r1))", "var1": "1(NegatedAtom in(box1, r3))", "var2": "0(Atom position(r1))", "var3": "1(NegatedAtom in(box1, r2))", "var4": "0(Atom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "0(Atom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "4(Atom is_empty(left))", "var13": "4(Atom is_empty(right))", "var14": "1(NegatedAtom in(small2, r1))", "var15": "0(Atom in(small3, r1))", "var16": "0(Atom in(small4, r1))"}, "new_parent": [54, "push box1 r2 r1", 1]}
+{"ts": 18, "act": "generated_states", "state_id": 54, "op": "push box1 r2 r1"}
+{"ts":18, "act": "state_update", "state_id": 56, "g": -1, "real_g": -1}
+{"ts": 18, "act": "evaluated_states", "state_id": 56, "preferred": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 18}
+{"ts":18, "act": "state_update", "state_id": 56, "h_ff": 3}
+{"ts": 18, "act": "evaluated_heuristics", "count": 1}
+{"ts":18, "act": "state_update", "state_id": 57, "state": {"var0": "1(NegatedAtom in(box1, r1))", "var1": "0(Atom in(box1, r3))", "var2": "2(Atom position(r3))", "var3": "1(NegatedAtom in(box1, r2))", "var4": "0(Atom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "0(Atom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "4(Atom is_empty(left))", "var13": "4(Atom is_empty(right))", "var14": "1(NegatedAtom in(small2, r1))", "var15": "0(Atom in(small3, r1))", "var16": "0(Atom in(small4, r1))"}, "new_parent": [54, "push box1 r2 r3", 1]}
+{"ts": 18, "act": "generated_states", "state_id": 54, "op": "push box1 r2 r3"}
+{"ts":18, "act": "state_update", "state_id": 57, "g": -1, "real_g": -1}
+{"ts": 18, "act": "evaluated_states", "state_id": 57, "preferred": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 18}
+{"ts":18, "act": "state_update", "state_id": 57, "h_ff": 4}
+{"ts": 18, "act": "evaluated_heuristics", "count": 1}
+{"ts":18, "act": "state_update", "state_id": 58, "state": {"var0": "1(NegatedAtom in(box1, r1))", "var1": "1(NegatedAtom in(box1, r3))", "var2": "1(Atom position(r2))", "var3": "0(Atom in(box1, r2))", "var4": "0(Atom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "0(Atom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "1(Atom hold_on(left, small2))", "var13": "4(Atom is_empty(right))", "var14": "1(NegatedAtom in(small2, r1))", "var15": "0(Atom in(small3, r1))", "var16": "0(Atom in(small4, r1))"}, "new_parent": [54, "grab small2 r2 left", 1]}
+{"ts": 18, "act": "generated_states", "state_id": 54, "op": "grab small2 r2 left"}
+{"ts":18, "act": "state_update", "state_id": 58, "g": -1, "real_g": -1}
+{"ts": 18, "act": "evaluated_states", "state_id": 58, "preferred": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 18}
+{"ts":18, "act": "state_update", "state_id": 58, "h_ff": 2}
+{"ts": 18, "act": "evaluated_heuristics", "count": 1}
+{"ts":18, "act": "state_update", "state_id": 59, "state": {"var0": "1(NegatedAtom in(box1, r1))", "var1": "1(NegatedAtom in(box1, r3))", "var2": "1(Atom position(r2))", "var3": "0(Atom in(box1, r2))", "var4": "0(Atom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "0(Atom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "4(Atom is_empty(left))", "var13": "1(Atom hold_on(right, small2))", "var14": "1(NegatedAtom in(small2, r1))", "var15": "0(Atom in(small3, r1))", "var16": "0(Atom in(small4, r1))"}, "new_parent": [54, "grab small2 r2 right", 1]}
+{"ts": 18, "act": "generated_states", "state_id": 54, "op": "grab small2 r2 right"}
+{"ts":18, "act": "state_update", "state_id": 59, "g": -1, "real_g": -1}
+{"ts": 18, "act": "evaluated_states", "state_id": 59, "preferred": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 18}
+{"ts":18, "act": "state_update", "state_id": 59, "h_ff": 2}
+{"ts": 18, "act": "evaluated_heuristics", "count": 1}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 19}
+{"ts":19, "act": "state_update", "state_id": 58, "g": 13, "real_g": 13}
+{"ts": 19, "act": "expanded_states", "state_id": 58}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 19}
+{"ts":19, "act": "state_update", "state_id": 60, "state": {"var0": "1(NegatedAtom in(box1, r1))", "var1": "1(NegatedAtom in(box1, r3))", "var2": "0(Atom position(r1))", "var3": "0(Atom in(box1, r2))", "var4": "0(Atom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "0(Atom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "1(Atom hold_on(left, small2))", "var13": "4(Atom is_empty(right))", "var14": "1(NegatedAtom in(small2, r1))", "var15": "0(Atom in(small3, r1))", "var16": "0(Atom in(small4, r1))"}, "new_parent": [58, "move r2 r1", 1]}
+{"ts": 19, "act": "generated_states", "state_id": 58, "op": "move r2 r1"}
+{"ts":19, "act": "state_update", "state_id": 60, "g": -1, "real_g": -1}
+{"ts": 19, "act": "evaluated_states", "state_id": 60, "preferred": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 19}
+{"ts":19, "act": "state_update", "state_id": 60, "h_ff": 1}
+{"ts": 19, "act": "evaluated_heuristics", "count": 1}
+{"ts":19, "act": "state_update", "state_id": 61, "state": {"var0": "1(NegatedAtom in(box1, r1))", "var1": "1(NegatedAtom in(box1, r3))", "var2": "2(Atom position(r3))", "var3": "0(Atom in(box1, r2))", "var4": "0(Atom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "0(Atom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "1(Atom hold_on(left, small2))", "var13": "4(Atom is_empty(right))", "var14": "1(NegatedAtom in(small2, r1))", "var15": "0(Atom in(small3, r1))", "var16": "0(Atom in(small4, r1))"}, "new_parent": [58, "move r2 r3", 1]}
+{"ts": 19, "act": "generated_states", "state_id": 58, "op": "move r2 r3"}
+{"ts":19, "act": "state_update", "state_id": 61, "g": -1, "real_g": -1}
+{"ts": 19, "act": "evaluated_states", "state_id": 61, "preferred": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 19}
+{"ts":19, "act": "state_update", "state_id": 61, "h_ff": 3}
+{"ts": 19, "act": "evaluated_heuristics", "count": 1}
+{"ts":19, "act": "state_update", "state_id": 62, "state": {"var0": "0(Atom in(box1, r1))", "var1": "1(NegatedAtom in(box1, r3))", "var2": "0(Atom position(r1))", "var3": "1(NegatedAtom in(box1, r2))", "var4": "0(Atom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "0(Atom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "1(Atom hold_on(left, small2))", "var13": "4(Atom is_empty(right))", "var14": "1(NegatedAtom in(small2, r1))", "var15": "0(Atom in(small3, r1))", "var16": "0(Atom in(small4, r1))"}, "new_parent": [58, "push box1 r2 r1", 1]}
+{"ts": 19, "act": "generated_states", "state_id": 58, "op": "push box1 r2 r1"}
+{"ts":19, "act": "state_update", "state_id": 62, "g": -1, "real_g": -1}
+{"ts": 19, "act": "evaluated_states", "state_id": 62, "preferred": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 19}
+{"ts":19, "act": "state_update", "state_id": 62, "h_ff": 1}
+{"ts": 19, "act": "evaluated_heuristics", "count": 1}
+{"ts":19, "act": "state_update", "state_id": 63, "state": {"var0": "1(NegatedAtom in(box1, r1))", "var1": "0(Atom in(box1, r3))", "var2": "2(Atom position(r3))", "var3": "1(NegatedAtom in(box1, r2))", "var4": "0(Atom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "0(Atom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "1(Atom hold_on(left, small2))", "var13": "4(Atom is_empty(right))", "var14": "1(NegatedAtom in(small2, r1))", "var15": "0(Atom in(small3, r1))", "var16": "0(Atom in(small4, r1))"}, "new_parent": [58, "push box1 r2 r3", 1]}
+{"ts": 19, "act": "generated_states", "state_id": 58, "op": "push box1 r2 r3"}
+{"ts":19, "act": "state_update", "state_id": 63, "g": -1, "real_g": -1}
+{"ts": 19, "act": "evaluated_states", "state_id": 63, "preferred": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 19}
+{"ts":19, "act": "state_update", "state_id": 63, "h_ff": 3}
+{"ts": 19, "act": "evaluated_heuristics", "count": 1}
+{"ts":19, "act": "state_update", "state_id": 64, "state": {"var0": "1(NegatedAtom in(box1, r1))", "var1": "1(NegatedAtom in(box1, r3))", "var2": "1(Atom position(r2))", "var3": "0(Atom in(box1, r2))", "var4": "0(Atom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "0(Atom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "1(Atom hold_on(left, small2))", "var13": "1(Atom hold_on(right, small2))", "var14": "1(NegatedAtom in(small2, r1))", "var15": "0(Atom in(small3, r1))", "var16": "0(Atom in(small4, r1))"}, "new_parent": [58, "grab small2 r2 right", 1]}
+{"ts": 19, "act": "generated_states", "state_id": 58, "op": "grab small2 r2 right"}
+{"ts":19, "act": "state_update", "state_id": 64, "g": -1, "real_g": -1}
+{"ts": 19, "act": "evaluated_states", "state_id": 64, "preferred": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 19}
+{"ts":19, "act": "state_update", "state_id": 64, "h_ff": 2}
+{"ts": 19, "act": "evaluated_heuristics", "count": 1}
+{"ts":19, "act": "state_update", "state_id": 54, "new_parent": [58, "release small2 r2 left", 1]}
+{"ts": 19, "act": "generated_states", "state_id": 58, "op": "release small2 r2 left"}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 20}
+{"ts":20, "act": "state_update", "state_id": 60, "g": 14, "real_g": 14}
+{"ts": 20, "act": "expanded_states", "state_id": 60}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 20}
+{"ts":20, "act": "state_update", "state_id": 58, "new_parent": [60, "move r1 r2", 1]}
+{"ts": 20, "act": "generated_states", "state_id": 60, "op": "move r1 r2"}
+{"ts":20, "act": "state_update", "state_id": 65, "state": {"var0": "1(NegatedAtom in(box1, r1))", "var1": "1(NegatedAtom in(box1, r3))", "var2": "0(Atom position(r1))", "var3": "0(Atom in(box1, r2))", "var4": "0(Atom light(r2))", "var5": "1(NegatedAtom light(r1))", "var6": "0(Atom light(r3))", "var7": "1(NegatedAtom in(small1, r2))", "var8": "1(NegatedAtom in(small1, r3))", "var9": "1(NegatedAtom in(small2, r3))", "var10": "1(NegatedAtom in(small3, r2))", "var11": "1(NegatedAtom in(small4, r2))", "var12": "4(Atom is_empty(left))", "var13": "4(Atom is_empty(right))", "var14": "0(Atom in(small2, r1))", "var15": "0(Atom in(small3, r1))", "var16": "0(Atom in(small4, r1))"}, "new_parent": [60, "release small2 r1 left", 1]}
+{"ts": 20, "act": "generated_states", "state_id": 60, "op": "release small2 r1 left"}
+{"ts":20, "act": "state_update", "state_id": 65, "g": -1, "real_g": -1}
+{"ts": 20, "act": "evaluated_states", "state_id": 65, "preferred": 0}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 20}
+{"ts":20, "act": "state_update", "state_id": 65, "h_ff": 0}
+{"ts": 20, "act": "evaluated_heuristics", "count": 1}
+{"act": "end_group"}
+{"act": "begin_group", "ts": 21}
+{"ts":21, "act": "state_update", "state_id": 65, "g": 15, "real_g": 15}
+{"ts": 21, "act": "expanded_states", "state_id": 65}
+{"act": "end_group"}
+{"ts": 21, "act": "plan_found", "state_ids": [0, 1, 3, 8, 12, 15, 19, 26, 43, 45, 50, 53, 54, 58, 60, 65]}

lab5/QLearningController.java

+import java.text.DecimalFormat;
+import java.text.NumberFormat;
+import java.util.Hashtable;
+import java.util.Locale;
+import java.util.Random;
+
+// TDDC17 - Lab 5
+// Part 2: Q Learning Controller
+// Last modification: 2020-10-06
+
+/* GUI commands :
+ * 		M : disable GUI, much faster
+ * 		E : toggle exploration
+ * 		V : Speed up learning
+ * 		P : turn off controller
+ * 		O : turn on controller
+ * 		W, A, S, D : manual commands
+ */
+
+/* TODO: 
+ * -Define state and reward functions (StateAndReward.java) suitable for your problem --> done.
+ * -Define actions --> done.
+ * -Implement missing parts of Q-learning --> done.
+ * -Tune state and reward function, and parameters below if the result is not satisfactory */
+
+public class QLearningController extends Controller {
+	// Change this value for part 3 (State & reward hover)
+	boolean HOVER_MODE = true;
+	
+	/* These are the agents senses (inputs) */
+	DoubleFeature x; /* Positions */
+	DoubleFeature y;
+	DoubleFeature vx; /* Velocities */
+	DoubleFeature vy;
+	DoubleFeature angle; /* Angle */
+
+	/* These are the agents actuators (outputs)*/
+	RocketEngine leftEngine;
+	RocketEngine middleEngine;
+	RocketEngine rightEngine;
+
+	final static int NUM_ACTIONS = 7; /* The takeAction function must be changed if this is modified */
+	
+	/* Keep track of the previous state and action */
+	String previous_state = null;
+	double previous_vx = 0;
+	double previous_vy = 0;
+	double previous_angle = 0;
+	int previous_action = 0; 
+	
+	/* The tables used by Q-learning */
+	Hashtable<String, Double> Qtable = new Hashtable<String, Double>(); /* Contains the Q-values - the state-action utilities */
+	Hashtable<String, Integer> Ntable = new Hashtable<String, Integer>(); /* Keeps track of how many times each state-action combination has been used */
+
+	/* PARAMETERS OF THE LEARNING ALGORITHM - THESE MAY BE TUNED BUT THE DEFAULT VALUES OFTEN WORK REASONABLY WELL  */
+	static final double GAMMA_DISCOUNT_FACTOR = 0.95; /* Must be < 1, small values make it very greedy */
+	static final double LEARNING_RATE_CONSTANT = 10; /* See alpha(), lower values are good for quick results in large and deterministic state spaces */
+	double explore_chance = 0.5; /* The exploration chance during the exploration phase */
+	final static int REPEAT_ACTION_MAX = 30; /* Repeat selected action at most this many times trying reach a new state, without a max it could loop forever if the action cannot lead to a new state */
+
+	/* Some internal counters */
+	int iteration = 0; /* Keeps track of how many iterations the agent has run */
+	int action_counter = 0; /* Keeps track of how many times we have repeated the current action */
+	int print_counter = 0; /* Makes printouts less spammy */ 
+
+	/* These are just internal helper variables, you can ignore these */
+	boolean paused = false;
+	boolean explore = true; /* Will always do exploration by default */
+
+	DecimalFormat df = (DecimalFormat) NumberFormat.getNumberInstance(Locale.US); 
+	public SpringObject object;
+	ComposedSpringObject cso;
+	long lastPressedExplore = 0;
+
+	public void init() {
+		cso = (ComposedSpringObject) object;
+		x = (DoubleFeature) cso.getObjectById("x");
+		y = (DoubleFeature) cso.getObjectById("y");
+		vx = (DoubleFeature) cso.getObjectById("vx");
+		vy = (DoubleFeature) cso.getObjectById("vy");
+		angle = (DoubleFeature) cso.getObjectById("angle");
+		
+		previous_vy = vy.getValue();
+		previous_vx = vx.getValue();
+		previous_angle = angle.getValue();
+
+		leftEngine = (RocketEngine) cso.getObjectById("rocket_engine_left");
+		rightEngine = (RocketEngine) cso.getObjectById("rocket_engine_right");
+		middleEngine = (RocketEngine) cso.getObjectById("rocket_engine_middle");
+	}
+
+	
+	/* Turn off all rockets */
+	void resetRockets() {
+		leftEngine.setBursting(false);
+		rightEngine.setBursting(false);
+		middleEngine.setBursting(false);
+	}
+
+	/* Performs the chosen action */
+	void performAction(int action) {
+		/* Fire zeh rockets! */
+		resetRockets();
+		switch (action) {
+			// Do nothing
+			case 0: break;
+			
+			case 1: leftEngine.setBursting(true); 
+			break;
+			
+			case 2: rightEngine.setBursting(true); 
+			break;
+			
+			case 3: middleEngine.setBursting(true); 
+			break;
+			
+			case 4: leftEngine.setBursting(true); 
+			middleEngine.setBursting(true); 
+			break;
+			
+			case 5: rightEngine.setBursting(true);
+			middleEngine.setBursting(true); 
+			break;
+			
+			case 6: middleEngine.setBursting(true);
+			leftEngine.setBursting(true);
+			rightEngine.setBursting(true); 
+			break;
+			
+			case 7: rightEngine.setBursting(true);
+			leftEngine.setBursting(true); 
+			break;
+		}
+	}
+
+	/* Main decision loop. Called every iteration by the simulator */
+	public void tick(int currentTime) {
+		iteration++;
+		
+		if (!paused) {
+			String new_state;
+			double previous_reward;
+			
+			// You can choose the value of HOVER_MODE line 19 (for part 3)
+			if (!HOVER_MODE) {
+				new_state = StateAndReward.getStateAngle(angle.getValue(), vx.getValue(), vy.getValue());
+				previous_reward = StateAndReward.getRewardAngle(previous_angle, previous_vx, previous_vy);
+			}
+			else {
+				new_state = StateAndReward.getStateHover(angle.getValue(), vx.getValue(), vy.getValue());
+				previous_reward = StateAndReward.getRewardHover(previous_angle, previous_vx, previous_vy);
+			}
+
+			/* Repeat the chosen action for a while, hoping to reach a new state. This is a trick to speed up learning on this problem. */
+			action_counter++;
+			if (new_state.equals(previous_state) && action_counter < REPEAT_ACTION_MAX) {
+				return;
+			}
+
+			action_counter = 0;
+
+			/* The agent is in a new state, do learning and action selection */
+			if (previous_state != null) {
+				/* Create state-action key */
+				String prev_stateaction = previous_state + previous_action;
+
+				/* Increment state-action counter */
+				if (Ntable.get(prev_stateaction) == null) {
+					Ntable.put(prev_stateaction, 0);
+				}
+				Ntable.put(prev_stateaction, Ntable.get(prev_stateaction) + 1);
+
+				/* Update Q value */
+				if (Qtable.get(prev_stateaction) == null) {
+					Qtable.put(prev_stateaction, 0.0);
+				} else {
+					// Here, we use temporal-difference Q-learning equation, as define in
+					// AI: A Modern Approach, 4th ed. (Russell, Norvig) p. 802 (22.7):
+					// Q(s,a) <-- Q(s,a) + alpha*[R(s,a,s') + gamma*max(a')(Q(s',a')) - Q(s,a)]
+					// where: 	alpha --> learning rate
+					//			gamma --> discount factor
+					
+					double gamma = GAMMA_DISCOUNT_FACTOR;
+					double alpha = alpha(Ntable.get(prev_stateaction));
+					
+					double Q = Qtable.get(prev_stateaction) 
+							+ alpha*(previous_reward + gamma*getMaxActionQValue(new_state) - Qtable.get(prev_stateaction));
+					
+					Qtable.put(prev_stateaction, Q);
+					
+					// done.
+				}
+				
+				int action = selectAction(new_state); /* Make sure you understand how it selects an action */
+
+				performAction(action);
+				
+				/* Only print every 10th line to reduce spam */
+				print_counter++;
+				if (print_counter % 1000 == 0) {
+					System.out.println("ITERATION: " + iteration + " SENSORS: a=" + df.format(angle.getValue()) + " vx=" + df.format(vx.getValue()) + 
+							" vy=" + df.format(vy.getValue()) + " P_STATE: " + previous_state + " P_ACTION: " + previous_action + 
+							" P_REWARD: " + df.format(previous_reward) + " P_QVAL: " + df.format(Qtable.get(prev_stateaction)) + " Tested: "
+							+ Ntable.get(prev_stateaction) + " times.");
+				}
+				
+				previous_vy = vy.getValue();
+				previous_vx = vx.getValue();
+				previous_angle = angle.getValue();
+				previous_action = action;
+			}
+			previous_state = new_state;
+		}
+
+	}
+
+	/* Computes the learning rate parameter alpha based on the number of times the state-action combination has been tested */
+	public double alpha(int num_tested) {
+		/* Lower learning rate constants means that alpha will become small faster and therefore make the agent behavior converge to 
+		 * to a solution faster, but if the state space is not properly explored at that point the resulting behavior may be poor.
+		 * If your state-space is really huge you may need to increase it. */
+		double alpha = (LEARNING_RATE_CONSTANT/(LEARNING_RATE_CONSTANT + num_tested));
+		return alpha;
+	}
+	
+	/* Finds the highest Qvalue of any action in the given state */
+	public double getMaxActionQValue(String state) {
+		double maxQval = Double.NEGATIVE_INFINITY;
+		
+		for (int action = 0; action < NUM_ACTIONS; action++) {
+			Double Qval = Qtable.get(state+action);
+			if (Qval != null && Qval > maxQval) {
+				maxQval = Qval;
+			} 
+		}
+
+		if (maxQval == Double.NEGATIVE_INFINITY) {
+			/* Assign 0 as that corresponds to initializing the Qtable to 0. */
+			maxQval = 0;
+		}
+		return maxQval;
+	}
+	
+	/* Selects an action in a state based on the registered Q-values and the exploration chance */
+	public int selectAction(String state) {
+		Random rand = new Random();
+
+		int action = 0;
+		/* May do exploratory move if in exploration mode */
+		if (explore && Math.abs(rand.nextDouble()) < explore_chance) {
+			/* Taking random exploration action! */
+			action = Math.abs(rand. nextInt()) % NUM_ACTIONS;
+			return action;
+		}
+
+		/* Find action with highest Q-val (utility) in given state */
+		double maxQval = Double.NEGATIVE_INFINITY;
+		for (int i = 0; i < NUM_ACTIONS; i++) {
+			String test_pair = state + i; /* Generate a state-action pair for all actions */
+			double Qval = 0;
+			if (Qtable.get(test_pair) != null) {
+				Qval = Qtable.get(test_pair);
+			}
+			if (Qval > maxQval) {
+				maxQval = Qval;
+				action = i;
+			}
+		}
+		return action;
+	}
+
+	
+	
+	/* The 'E' key will toggle the agents exploration mode. Turn this off to test its behavior */
+	public void toggleExplore() {
+		/* Make sure we don't toggle it multiple times */
+		if (System.currentTimeMillis() - lastPressedExplore < 1000) {
+			return;
+		}
+		if (explore) {
+			System.out.println("Turning OFF exploration!");
+			explore = false;
+		} else {
+			System.out.println("Turning ON exploration!");
+			explore = true;
+		}
+		lastPressedExplore = System.currentTimeMillis(); 
+	}
+
+	/* Keys 1 and 2 can be customized for whatever purpose if you want to */
+	public void toggleCustom1() {
+		System.out.println("Custom key 1 pressed!");
+	}
+
+	/* Keys 1 and 2 can be customized for whatever purpose if you want to */
+	public void toggleCustom2() {
+		System.out.println("Custom key 2 pressed!");
+	}
+	
+	public void pause() {
+		paused = true;
+		resetRockets();
+	}
+
+	public void run() {
+		paused = false;
+	}
+}

lab5/StateAndReward.java

+public class StateAndReward {
+
+	
+	/* State discretization function for the angle controller */
+	public static String getStateAngle(double angle, double vx, double vy) {		
+		return " " + discretize2(angle, 20, 2*-Math.PI/3, 2*Math.PI/3) + " ";
+	}
+
+	/* Reward function for the angle controller */
+	public static double getRewardAngle(double angle, double vx, double vy) {
+		return Math.PI - Math.abs(angle);
+	}
+
+	/* State discretization function for the full hover controller */
+	public static String getStateHover(double angle, double vx, double vy) {
+		return discretize2(angle, 10, 2*-Math.PI/3, 2*Math.PI/3) 
+			+ " / " + discretize(vx, 3, -1, 1) 
+			+ " / " + discretize(vy, 11, -1, 1);
+	}
+
+	/* Reward function for the full hover controller */
+	public static double getRewardHover(double angle, double vx, double vy) {
+		return (Math.PI - Math.abs(angle))/2 + (10 - Math.abs(Math.max(Math.abs(vx), Math.abs(vy)))) / 5;
+	}
+
+	// ///////////////////////////////////////////////////////////
+	// discretize() performs a uniform discretization of the
+	// value parameter.
+	// It returns an integer between 0 and nrValues-1.
+	// The min and max parameters are used to specify the interval
+	// for the discretization.
+	// If the value is lower than min, 0 is returned
+	// If the value is higher than min, nrValues-1 is returned
+	// otherwise a value between 1 and nrValues-2 is returned.
+	//
+	// Use discretize2() if you want a discretization method that does
+	// not handle values lower than min and higher than max.
+	// ///////////////////////////////////////////////////////////
+	public static int discretize(double value, int nrValues, double min,
+			double max) {
+		if (nrValues < 2) {
+			return 0;
+		}
+
+		double diff = max - min;
+
+		if (value < min) {
+			return 0;
+		}
+		if (value > max) {
+			return nrValues - 1;
+		}
+
+		double tempValue = value - min;
+		double ratio = tempValue / diff;
+
+		return (int) (ratio * (nrValues - 2)) + 1;
+	}
+
+	// ///////////////////////////////////////////////////////////
+	// discretize2() performs a uniform discretization of the
+	// value parameter.
+	// It returns an integer between 0 and nrValues-1.
+	// The min and max parameters are used to specify the interval
+	// for the discretization.
+	// If the value is lower than min, 0 is returned
+	// If the value is higher than min, nrValues-1 is returned
+	// otherwise a value between 0 and nrValues-1 is returned.
+	// ///////////////////////////////////////////////////////////
+	public static int discretize2(double value, int nrValues, double min,
+			double max) {
+		double diff = max - min;
+
+		if (value < min) {
+			return 0;
+		}
+		if (value > max) {
+			return nrValues - 1;
+		}
+
+		double tempValue = value - min;
+		double ratio = tempValue / diff;
+
+		return (int) (ratio * nrValues);
+	}
+
+}

lab5/TutorialController.java

+// INFO: See below.
+
+public class TutorialController extends Controller {
+
+    public SpringObject object;
+
+    ComposedSpringObject cso;
+
+    /* These are the agents senses (inputs) */
+	DoubleFeature x; /* Positions */
+	DoubleFeature y;
+	DoubleFeature vx; /* Velocities */
+	DoubleFeature vy;
+	DoubleFeature angle; /* Angle */
+
+    /* Example:
+     * x.getValue() returns the vertical position of the rocket 
+     */
+
+	/* These are the agents actuators (outputs)*/
+	RocketEngine leftRocket;
+	RocketEngine middleRocket;
+	RocketEngine rightRocket;
+
+    /* Example:
+     * leftRocket.setBursting(true) turns on the left rocket 
+     */
+	
+	public void init() {
+		cso = (ComposedSpringObject) object;
+		x = (DoubleFeature) cso.getObjectById("x");
+		y = (DoubleFeature) cso.getObjectById("y");
+		vx = (DoubleFeature) cso.getObjectById("vx");
+		vy = (DoubleFeature) cso.getObjectById("vy");
+		angle = (DoubleFeature) cso.getObjectById("angle");
+
+		leftRocket = (RocketEngine) cso.getObjectById("rocket_engine_left");
+		rightRocket = (RocketEngine) cso.getObjectById("rocket_engine_right");
+		middleRocket = (RocketEngine) cso.getObjectById("rocket_engine_middle");
+	}
+
+    public void tick(int currentTime) {
+
+    	// TDDC17
+    	// Part1: Print useful informations and use 
+    	// threshold to guide the vessel
+    	
+    	System.out.println("Tick: " + currentTime + ". Angle: " + angle.getValue() + ", vx: " + vx.getValue() + ", vy: " + vy.getValue());
+    	
+    	
+    	System.out.print("Vessel state: ");
+    	if (angle.getValue() > 0.01) {
+    		rightRocket.setBursting(true);
+    		System.out.print("ENG_RGT ON; ");
+    	}
+    	else {
+    		rightRocket.setBursting(false);
+    		System.out.print("ENG_RGT OFF; ");
+    	}
+    	if (angle.getValue() < -0.01) {
+    		leftRocket.setBursting(true);
+   			System.out.print("ENG_LFT ON; ");
+    	}
+    	else {
+    		leftRocket.setBursting(false);
+    		System.out.print("ENG_LFT OFF; ");
+    	}
+    	if (vy.getValue() > 0.05) {
+    		middleRocket.setBursting(true);
+    		System.out.print("ENG_MDL ON; ");
+    	}
+    	else {
+    		middleRocket.setBursting(false);
+    		System.out.print("ENG_MDL OFF; ");
+    	}
+    	
+    	System.out.println("");
+    }
+
+}