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GNU GENERAL PUBLIC LICENSE
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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
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You should have received a copy of the GNU General Public License
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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>.

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# 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
```

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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

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

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lab2/CustomDepthFirstSearch.java Normal file
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package searchCustom;
public class CustomDepthFirstSearch extends CustomGraphSearch{
public CustomDepthFirstSearch(int maxDepth){
super(true);
//System.out.println("Change line above in \"CustomDepthFirstSearch.java\"! --> done.");
}
};

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lab2/CustomGraphSearch.java Normal file
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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;
}
}

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lab3/bayes.jnlp Normal file
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<?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>

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lab3/lab3_Mr_HS_v2.xml Normal file
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<?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>

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<?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>

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<?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>

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<?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>

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## 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)
```

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;; 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)))
)
)

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;; 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))
)
)

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(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))
)
)

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#!/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

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{"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}
{"ts":1, "act": "state_update", "state_id": 2, "state": {"var0": "0(Atom in(box1, r1))", "var1": "1(NegatedAtom in(box1, r3))", "var2": "2(Atom position(r3))", "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 r3", 1]}
{"ts": 1, "act": "generated_states", "state_id": 0, "op": "move r2 r3"}
{"ts":1, "act": "state_update", "state_id": 2, "g": -1, "real_g": -1}
{"ts": 1, "act": "evaluated_states", "state_id": 2, "preferred": 0}
{"act": "end_group"}
{"act": "begin_group", "ts": 1}
{"ts":1, "act": "state_update", "state_id": 2, "h_ff": 12}
{"ts": 1, "act": "evaluated_heuristics", "count": 1}
{"act": "end_group"}
{"act": "begin_group", "ts": 2}
{"ts":2, "act": "state_update", "state_id": 1, "g": 1, "real_g": 1}
{"ts": 2, "act": "expanded_states", "state_id": 1}
{"act": "end_group"}
{"act": "begin_group", "ts": 2}
{"ts":2, "act": "state_update", "state_id": 3, "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": "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": [1, "push box1 r1 r2", 1]}
{"ts": 2, "act": "generated_states", "state_id": 1, "op": "push box1 r1 r2"}
{"ts":2, "act": "state_update", "state_id": 3, "g": -1, "real_g": -1}
{"ts": 2, "act": "evaluated_states", "state_id": 3, "preferred": 0}
{"act": "end_group"}
{"act": "begin_group", "ts": 2}
{"ts":2, "act": "state_update", "state_id": 3, "h_ff": 11}
{"ts": 2, "act": "evaluated_heuristics", "count": 1}
{"ts":2, "act": "state_update", "state_id": 4, "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": "0(Atom 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": [1, "turn_on_light r1 box1", 1]}
{"ts": 2, "act": "generated_states", "state_id": 1, "op": "turn_on_light r1 box1"}
{"ts":2, "act": "state_update", "state_id": 4, "g": -1, "real_g": -1}
{"ts": 2, "act": "evaluated_states", "state_id": 4, "preferred": 0}
{"act": "end_group"}
{"act": "begin_group", "ts": 2}
{"ts":2, "act": "state_update", "state_id": 4, "h_ff": 12}
{"ts": 2, "act": "evaluated_heuristics", "count": 1}
{"ts":2, "act": "state_update", "state_id": 0, "new_parent": [1, "move r1 r2", 1]}
{"ts": 2, "act": "generated_states", "state_id": 1, "op": "move r1 r2"}
{"act": "end_group"}
{"act": "begin_group", "ts": 3}
{"ts":3, "act": "state_update", "state_id": 3, "g": 2, "real_g": 2}
{"ts": 3, "act": "expanded_states", "state_id": 3}
{"act": "end_group"}
{"act": "begin_group", "ts": 3}
{"ts":3, "act": "state_update", "state_id": 5, "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": "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": [3, "move r2 r1", 1]}
{"ts": 3, "act": "generated_states", "state_id": 3, "op": "move r2 r1"}
{"ts":3, "act": "state_update", "state_id": 5, "g": -1, "real_g": -1}
{"ts": 3, "act": "evaluated_states", "state_id": 5, "preferred": 0}
{"act": "end_group"}
{"act": "begin_group", "ts": 3}
{"ts":3, "act": "state_update", "state_id": 5, "h_ff": 11}
{"ts": 3, "act": "evaluated_heuristics", "count": 1}
{"ts":3, "act": "state_update", "state_id": 6, "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": "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": [3, "move r2 r3", 1]}
{"ts": 3, "act": "generated_states", "state_id": 3, "op": "move r2 r3"}
{"ts":3, "act": "state_update", "state_id": 6, "g": -1, "real_g": -1}
{"ts": 3, "act": "evaluated_states", "state_id": 6, "preferred": 0}
{"act": "end_group"}
{"act": "begin_group", "ts": 3}
{"ts":3, "act": "state_update", "state_id": 6, "h_ff": 11}
{"ts": 3, "act": "evaluated_heuristics", "count": 1}
{"ts":3, "act": "state_update", "state_id": 1, "new_parent": [3, "push box1 r2 r1", 1]}
{"ts": 3, "act": "generated_states", "state_id": 3, "op": "push box1 r2 r1"}
{"ts":3, "act": "state_update", "state_id": 7, "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": "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": [3, "push box1 r2 r3", 1]}
{"ts": 3, "act": "generated_states", "state_id": 3, "op": "push box1 r2 r3"}
{"ts":3, "act": "state_update", "state_id": 7, "g": -1, "real_g": -1}
{"ts": 3, "act": "evaluated_states", "state_id": 7, "preferred": 0}
{"act": "end_group"}
{"act": "begin_group", "ts": 3}
{"ts":3, "act": "state_update", "state_id": 7, "h_ff": 11}
{"ts": 3, "act": "evaluated_heuristics", "count": 1}
{"ts":3, "act": "state_update", "state_id": 8, "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": "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": [3, "turn_on_light r2 box1", 1]}
{"ts": 3, "act": "generated_states", "state_id": 3, "op": "turn_on_light r2 box1"}
{"ts":3, "act": "state_update", "state_id": 8, "g": -1, "real_g": -1}
{"ts": 3, "act": "evaluated_states", "state_id": 8, "preferred": 0}
{"act": "end_group"}
{"act": "begin_group", "ts": 3}
{"ts":3, "act": "state_update", "state_id": 8, "h_ff": 10}
{"ts": 3, "act": "evaluated_heuristics", "count": 1}
{"act": "end_group"}
{"act": "begin_group", "ts": 4}
{"ts":4, "act": "state_update", "state_id": 8, "g": 3, "real_g": 3}
{"ts": 4, "act": "expanded_states", "state_id": 8}
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{"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]}

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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;
}
}

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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);
}
}

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// 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("");
}
}