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+                    GNU GENERAL PUBLIC LICENSE
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+                       Version 3, 29 June 2007
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+  12. No Surrender of Others' Freedom.
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+  13. Use with the GNU Affero General Public License.
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+  14. Revised Versions of this License.
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+  If the Program specifies that a proxy can decide which future
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+  17. Interpretation of Sections 15 and 16.
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+  If the disclaimer of warranty and limitation of liability provided
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+                     END OF TERMS AND CONDITIONS
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+            How to Apply These Terms to Your New Programs
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+
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+  If you develop a new program, and you want it to be of the greatest
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+possible use to the public, the best way to achieve this is to make it
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+free software which everyone can redistribute and change under these terms.
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+  To do so, attach the following notices to the program.  It is safest
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+the "copyright" line and a pointer to where the full notice is found.
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+    <one line to give the program's name and a brief idea of what it does.>
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+    Copyright (C) <year>  <name of author>
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+
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+    This program is free software: you can redistribute it and/or modify
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+    This program is distributed in the hope that it will be useful,
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+Also add information on how to contact you by electronic and paper mail.
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+  If the program does terminal interaction, make it output a short
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+    <program>  Copyright (C) <year>  <name of author>
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+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
657
+    This is free software, and you are welcome to redistribute it
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+    under certain conditions; type `show c' for details.
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+
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+The hypothetical commands `show w' and `show c' should show the appropriate
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+parts of the General Public License.  Of course, your program's commands
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+  You should also get your employer (if you work as a programmer) or school,
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+For more information on this, and how to apply and follow the GNU GPL, see
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+<https://www.gnu.org/licenses/>.
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+
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+  The GNU General Public License does not permit incorporating your program
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+into proprietary programs.  If your program is a subroutine library, you
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+the library.  If this is what you want to do, use the GNU Lesser General
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+Public License instead of this License.  But first, please read
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+<https://www.gnu.org/licenses/why-not-lgpl.html>.

+ 10
- 0
README.md View File

@@ -0,0 +1,10 @@
1
+# TDDC17AICourse
2
+Sharing of my labs carried out during the TDDC17 course at Linköping University (HT 2020).
3
+
4
+## Lab 4
5
+Commands used in lab 4 to use planner algorithms:
6
+```bash
7
+$ ipp -o task1_domain.pddl -f task1_pb1.pddl
8
+$ ff -o task1_domain.pddl -f task1_pb1.pddl
9
+$ lama task1_domain.pddl task1_pb1.pddl
10
+```

+ 434
- 0
lab1/myvacuumagent.py View File

@@ -0,0 +1,434 @@
1
+from lab1.liuvacuum import *
2
+
3
+DEBUG_OPT_DENSEWORLDMAP = False
4
+
5
+AGENT_STATE_UNKNOWN = 0
6
+AGENT_STATE_WALL = 1
7
+AGENT_STATE_CLEAR = 2
8
+AGENT_STATE_DIRT = 3
9
+AGENT_STATE_HOME = 4
10
+
11
+AGENT_DIRECTION_NORTH = 0
12
+AGENT_DIRECTION_EAST = 1
13
+AGENT_DIRECTION_SOUTH = 2
14
+AGENT_DIRECTION_WEST = 3
15
+
16
+def direction_to_string(cdr):
17
+    cdr %= 4
18
+    return  "NORTH" if cdr == AGENT_DIRECTION_NORTH else\
19
+            "EAST"  if cdr == AGENT_DIRECTION_EAST else\
20
+            "SOUTH" if cdr == AGENT_DIRECTION_SOUTH else\
21
+            "WEST" #if dir == AGENT_DIRECTION_WEST
22
+
23
+"""
24
+Internal state of a vacuum agent
25
+"""
26
+class MyAgentState:
27
+
28
+    def __init__(self, width, height):
29
+
30
+        # Initialize perceived world state
31
+        self.world = [[AGENT_STATE_UNKNOWN for _ in range(height)] for _ in range(width)]
32
+        self.world[1][1] = AGENT_STATE_HOME
33
+
34
+        # Agent internal state
35
+        self.last_action = ACTION_NOP
36
+        self.direction = AGENT_DIRECTION_EAST
37
+        self.pos_x = 1
38
+        self.pos_y = 1
39
+
40
+        # Metadata
41
+        self.world_width = width
42
+        self.world_height = height
43
+
44
+    """
45
+    Update perceived agent location and orientation
46
+    """
47
+    def update_position(self, bump):
48
+        if not bump and self.last_action == ACTION_FORWARD:
49
+            if self.direction == AGENT_DIRECTION_EAST:
50
+                self.pos_x += 1
51
+            elif self.direction == AGENT_DIRECTION_SOUTH:
52
+                self.pos_y += 1
53
+            elif self.direction == AGENT_DIRECTION_WEST:
54
+                self.pos_x -= 1
55
+            elif self.direction == AGENT_DIRECTION_NORTH:
56
+                self.pos_y -= 1
57
+        elif self.last_action == ACTION_TURN_LEFT:
58
+            if self.direction == AGENT_DIRECTION_EAST:
59
+                self.direction = AGENT_DIRECTION_NORTH
60
+            elif self.direction == AGENT_DIRECTION_SOUTH:
61
+                self.direction = AGENT_DIRECTION_EAST
62
+            elif self.direction == AGENT_DIRECTION_WEST:
63
+                self.direction = AGENT_DIRECTION_SOUTH
64
+            elif self.direction == AGENT_DIRECTION_NORTH:
65
+                self.direction = AGENT_DIRECTION_WEST
66
+        elif self.last_action == ACTION_TURN_RIGHT:
67
+            if self.direction == AGENT_DIRECTION_EAST:
68
+                self.direction = AGENT_DIRECTION_SOUTH
69
+            elif self.direction == AGENT_DIRECTION_SOUTH:
70
+                self.direction = AGENT_DIRECTION_WEST
71
+            elif self.direction == AGENT_DIRECTION_WEST:
72
+                self.direction = AGENT_DIRECTION_NORTH
73
+            elif self.direction == AGENT_DIRECTION_NORTH:
74
+                self.direction = AGENT_DIRECTION_EAST
75
+
76
+    """
77
+    Update perceived or inferred information about a part of the world
78
+    """
79
+    def update_world(self, x, y, info):
80
+        self.world[x][y] = info
81
+
82
+    """
83
+    Dumps a map of the world as the agent knows it
84
+    """
85
+    def print_world_debug(self):
86
+        for y in range(self.world_height):
87
+            for x in range(self.world_width):
88
+                if self.world[x][y] == AGENT_STATE_UNKNOWN:
89
+                    print("?" if DEBUG_OPT_DENSEWORLDMAP else " ? ", end="")
90
+                elif self.world[x][y] == AGENT_STATE_WALL:
91
+                    print("#" if DEBUG_OPT_DENSEWORLDMAP else " # ", end="")
92
+                elif self.world[x][y] == AGENT_STATE_CLEAR:
93
+                    print("." if DEBUG_OPT_DENSEWORLDMAP else " . ", end="")
94
+                elif self.world[x][y] == AGENT_STATE_DIRT:
95
+                    print("D" if DEBUG_OPT_DENSEWORLDMAP else " D ", end="")
96
+                elif self.world[x][y] == AGENT_STATE_HOME:
97
+                    print("H" if DEBUG_OPT_DENSEWORLDMAP else " H ", end="")
98
+
99
+            print() # Newline
100
+        print() # Delimiter post-print
101
+
102
+"""
103
+Vacuum agent
104
+"""
105
+class MyVacuumAgent(Agent):
106
+    """
107
+    Init function, everything here is execute once at the beginning
108
+    """
109
+    def __init__(self, world_width, world_height, log):
110
+        super().__init__(self.execute)
111
+        self.initial_random_actions = 10
112
+        self.iteration_counter = 1000
113
+        self.state = MyAgentState(world_width, world_height)
114
+        self.log = log
115
+
116
+        # Arrived variable for the goto function
117
+        self.arrived = True
118
+        # Arrived variable for the go_to_with_pathfinder function
119
+        self.arrived_destination = True
120
+        # If the agent has finished the cleaning
121
+        self.finished = False
122
+
123
+        # Goto coordinates for the goto function
124
+        self.goto_x = 1
125
+        self.goto_y = 1
126
+
127
+        # Goto coordinates for the go_to_with_pathfinder function
128
+        self.destination_x = 1
129
+        self.destination_y = 1
130
+
131
+        # If we need to re-calculate our destination
132
+        self.reset_destination = True
133
+
134
+        # Different list in agent memory
135
+        self.waypoint = []                  # Memory for next waypoint coordinates
136
+        self.slist = []                     # List of waypoint to go at the agent destination
137
+
138
+        # At the beginning, we assume that the zone is surrounded by wall
139
+        for i in range(0, world_width):
140
+            self.state.update_world(i, 0, AGENT_STATE_WALL)
141
+            self.state.update_world(i, world_height - 1, AGENT_STATE_WALL)
142
+        for j in range(1, world_height - 1):
143
+            self.state.update_world(0, j, AGENT_STATE_WALL)
144
+            self.state.update_world(world_width - 1, j, AGENT_STATE_WALL)
145
+
146
+    """
147
+    Function which move the agent to a ramdom start position
148
+    """
149
+    def move_to_random_start_position(self, bump):
150
+        action = random()
151
+
152
+        self.initial_random_actions -= 1
153
+        self.state.update_position(bump)
154
+        self.log("Actual direction: " + direction_to_string(self.state.direction))
155
+
156
+        if action < 0.1666666:   # 1/6 chance
157
+            self.state.last_action = ACTION_TURN_LEFT
158
+            return ACTION_TURN_LEFT
159
+        elif action < 0.3333333: # 1/6 chance
160
+            self.state.last_action = ACTION_TURN_RIGHT
161
+            return ACTION_TURN_RIGHT
162
+        else:                    # 4/6 chance
163
+            self.state.last_action = ACTION_FORWARD
164
+            return ACTION_FORWARD
165
+
166
+    """
167
+    A basic "go to" function which does not consider wall
168
+    """
169
+    def goto(self, bump, dirt):
170
+        self.state.update_position(bump)
171
+        self.log("Actual direction: " + direction_to_string(self.state.direction))
172
+        self.log("Next waypoint: {}, {}".format(self.goto_x, self.goto_y))
173
+
174
+        if dirt:
175
+            self.state.update_world(self.state.pos_x, self.state.pos_y, AGENT_STATE_DIRT)
176
+            self.log("DIRT -> choosing SUCK action!")
177
+            self.state.last_action = ACTION_SUCK
178
+            return ACTION_SUCK
179
+        else:
180
+            self.state.update_world(self.state.pos_x, self.state.pos_y, AGENT_STATE_CLEAR)
181
+
182
+        if (self.goto_x == self.state.pos_x and self.goto_y == self.state.pos_y) or bump:
183
+            self.arrived = True
184
+            self.log("Arrived to the target point")
185
+            self.state.last_action = ACTION_NOP
186
+            return ACTION_NOP
187
+
188
+        if abs(self.state.pos_x - self.goto_x) >= abs(self.state.pos_y - self.goto_y):
189
+            if self.state.pos_x - self.goto_x > 0:          # Need to go WEST
190
+                if self.state.direction == AGENT_DIRECTION_WEST:
191
+                    return ACTION_FORWARD
192
+                elif self.state.direction == AGENT_DIRECTION_SOUTH:
193
+                    return ACTION_TURN_RIGHT
194
+                else:
195
+                    return ACTION_TURN_LEFT
196
+            else:                                           # Need to go EAST
197
+                if self.state.direction == AGENT_DIRECTION_EAST:
198
+                    return ACTION_FORWARD
199
+                elif self.state.direction == AGENT_DIRECTION_NORTH:
200
+                    return ACTION_TURN_RIGHT
201
+                else:
202
+                    return ACTION_TURN_LEFT
203
+        else:
204
+            if self.state.pos_y - self.goto_y > 0:          # Need to go NORTH
205
+                if self.state.direction == AGENT_DIRECTION_NORTH:
206
+                    return ACTION_FORWARD
207
+                elif self.state.direction == AGENT_DIRECTION_WEST:
208
+                    return ACTION_TURN_RIGHT
209
+                else:
210
+                    return ACTION_TURN_LEFT
211
+            else:                                           # Need to go SOUTH
212
+                if self.state.direction == AGENT_DIRECTION_SOUTH:
213
+                    return ACTION_FORWARD
214
+                elif self.state.direction == AGENT_DIRECTION_EAST:
215
+                    return ACTION_TURN_RIGHT
216
+                else:
217
+                    return ACTION_TURN_LEFT
218
+
219
+    """
220
+    A pathfinding algorithm, based on the BFS algorithm
221
+    """
222
+    def pathfinding(self, target_x, target_y):
223
+
224
+        # Class for node used in the search
225
+        class Node:
226
+            def __init__(self, parent=None, x=-1, y=-1):
227
+                self.x = x
228
+                self.y = y
229
+                self.parent = parent
230
+
231
+            def __eq__(self, other):
232
+                return self.x == other.x and self.y == other.y
233
+
234
+        # Start and end nodes
235
+        start = Node(None, self.state.pos_x, self.state.pos_y)
236
+        end = Node(None, target_x, target_y)
237
+
238
+        # List containing the nodes to explore (By order of priority)
239
+        queue = [start]
240
+        # List of explored nodes
241
+        explored = []
242
+        # List which will be returned by the program containing
243
+        # the coordinates to follow to reach the targeted point
244
+        slist = []
245
+
246
+        # While there is node to explore
247
+        while queue:
248
+            # If the queue is to big, we assume that there is a problem
249
+            if len(queue) > 200:
250
+                break
251
+
252
+            # We take the first node in the queue
253
+            node = queue.pop(0)
254
+            # If it is the destination
255
+            if node == end:
256
+                print("Trying to go at : {}; {}".format(target_x, target_y))
257
+                print("Solution found : ")
258
+                n = node
259
+                # We return the path found
260
+                while n.parent:
261
+                    print("x: {}, y: {}".format(n.x, n.y))
262
+                    slist.append([n.x, n.y])
263
+                    n = n.parent
264
+                # and stop the processing
265
+                break
266
+
267
+            # If we never explored this node
268
+            elif node not in explored:
269
+                explored.append(node)
270
+
271
+                # For each adjacent node, if it is not a wall,
272
+                # we add it to the queue
273
+                if self.state.world[node.x - 1][node.y] != AGENT_STATE_WALL:
274
+                    queue.append(Node(node, node.x-1, node.y))
275
+                if self.state.world[node.x + 1][node.y] != AGENT_STATE_WALL:
276
+                    queue.append(Node(node, node.x+1, node.y))
277
+                if self.state.world[node.x][node.y - 1] != AGENT_STATE_WALL:
278
+                    queue.append(Node(node, node.x, node.y - 1))
279
+                if self.state.world[node.x][node.y + 1] != AGENT_STATE_WALL:
280
+                    queue.append(Node(node, node.x, node.y+1))
281
+
282
+        # If we don't find any solution
283
+        if not slist:
284
+            # We consider that the place in inaccessible
285
+            self.state.update_world(end.x, end.y, AGENT_STATE_WALL)
286
+            self.log("Find a inaccessible place, marking as a wall ##")
287
+            print("Overload! Inaccessible place!")
288
+        return slist
289
+
290
+    """
291
+    A "go to" function which implement the pathfinding algorithm using the goto function
292
+    """
293
+    def go_to_with_pathfinder(self, bump, dirt):
294
+        # If we start or if we need to reset the path
295
+        if self.reset_destination:
296
+            print("Launching the pathfinding algorithm.")
297
+            self.slist = self.pathfinding(self.destination_x, self.destination_y)
298
+
299
+        # If we have a path, we follow it
300
+        if self.slist:
301
+            # If the agent move correctly the previous time
302
+            if not bump:
303
+                self.waypoint = self.slist.pop()
304
+            self.goto_x = self.waypoint[0]
305
+            self.goto_y = self.waypoint[1]
306
+            self.arrived = False
307
+            action = self.goto(bump, dirt)
308
+            self.state.last_action = action
309
+            self.reset_destination = False
310
+            return action
311
+        # else (whether we arrived or we don't find a path), we assume
312
+        # that we will not go further
313
+        else:
314
+            self.reset_destination = True
315
+            self.arrived_destination = True
316
+            self.state.last_action = ACTION_NOP
317
+            return ACTION_NOP
318
+        pass
319
+
320
+    """
321
+    The execute function, which is call at each turn
322
+    """
323
+    def execute(self, percept):
324
+
325
+        # If the agent has finished, we do nothing
326
+        if self.finished:
327
+            return ACTION_NOP
328
+
329
+        ###########################
330
+        # DO NOT MODIFY THIS CODE #
331
+        ###########################
332
+
333
+        bump = percept.attributes["bump"]
334
+        dirt = percept.attributes["dirt"]
335
+        home = percept.attributes["home"]
336
+
337
+        # Move agent to a randomly chosen initial position
338
+        if self.initial_random_actions > 0:
339
+            self.log("Moving to random start position ({} steps left)".format(self.initial_random_actions))
340
+            return self.move_to_random_start_position(bump)
341
+
342
+        # Finalize randomization by properly updating position (without subsequently changing it)
343
+        elif self.initial_random_actions == 0:
344
+            self.initial_random_actions -= 1
345
+            self.state.update_position(bump)
346
+            self.state.last_action = ACTION_SUCK
347
+            self.log("Processing percepts after position randomization")
348
+            return ACTION_SUCK
349
+
350
+        ########################
351
+        # START MODIFYING HERE #
352
+        ########################
353
+
354
+        # Logging position and orientation
355
+        self.log("Position: ({}, {})\t\tDirection: {}".format(self.state.pos_x, self.state.pos_y,
356
+                                                              direction_to_string(self.state.direction)))
357
+
358
+        if bump:
359
+            # Not arrived, but this way we force our agent to re-calculate its path
360
+            self.arrived = True
361
+            self.reset_destination = True
362
+            # Get an xy-offset pair based on where the agent is facing
363
+            offset = [(0, -1), (1, 0), (0, 1), (-1, 0)][self.state.direction]
364
+
365
+            # Mark the tile at the offset from the agent as a wall (since the agent bumped into it)
366
+            self.state.update_world(self.state.pos_x + offset[0], self.state.pos_y + offset[1], AGENT_STATE_WALL)
367
+
368
+        # While we aren't arrived, we refer to the goto function
369
+        if not self.arrived:
370
+            action = self.goto(bump, dirt)
371
+            self.state.last_action = action
372
+            return action
373
+        # While we aren't arrived, we refer to the go_to_with_pathfinder function
374
+        elif not self.arrived_destination:
375
+            return self.go_to_with_pathfinder(bump, dirt)
376
+
377
+        # Max iterations for the agent
378
+        if self.iteration_counter < 1:
379
+            if self.iteration_counter == 0:
380
+                self.iteration_counter -= 1
381
+                self.log("Iteration counter is now 0. Halting!")
382
+                self.log("Performance: {}".format(self.performance))
383
+            return ACTION_NOP
384
+        self.iteration_counter -= 1
385
+
386
+        # Track position of agent
387
+        self.state.update_position(bump)
388
+
389
+        # Update perceived state of current tile
390
+        if dirt:
391
+            self.state.update_world(self.state.pos_x, self.state.pos_y, AGENT_STATE_DIRT)
392
+        else:
393
+            self.state.update_world(self.state.pos_x, self.state.pos_y, AGENT_STATE_CLEAR)
394
+
395
+        # Debug
396
+        self.state.print_world_debug()
397
+
398
+        # Variables used to determine the closest unknown place
399
+        closest_unk_range = self.state.world_height + self.state.world_width + 1
400
+        closest_x = 1
401
+        closest_y = 1
402
+        find_new = False
403
+
404
+        # Determination of the nearest unknown location
405
+        for i in range(self.state.world_width):
406
+            for j in range(self.state.world_height):
407
+                if self.state.world[i][j] == AGENT_STATE_UNKNOWN and (
408
+                        abs(self.state.pos_x - i) + abs(self.state.pos_y - j)) < closest_unk_range:
409
+                    find_new = True
410
+                    closest_unk_range = (abs(self.state.pos_x - i) + abs(self.state.pos_y - j))
411
+                    closest_x = i
412
+                    closest_y = j
413
+
414
+        # If we find a new place to go
415
+        if find_new:
416
+            self.log("Going to unknown places : ({};{})".format(closest_x, closest_y))
417
+            self.destination_x = closest_x
418
+            self.destination_y = closest_y
419
+            return self.go_to_with_pathfinder(bump, dirt)
420
+        # If not, we have finished, we go home
421
+        if home:
422
+            self.log("Finished !")
423
+            self.log("Performance: {}".format(self.performance))
424
+            self.finished = True
425
+        # Finally we are at home
426
+        else:
427
+            self.log("Job done, going back home.")
428
+            self.destination_x = 1
429
+            self.destination_y = 1
430
+            return self.go_to_with_pathfinder(bump, dirt)
431
+
432
+        # Useless
433
+        self.state.last_action = ACTION_NOP
434
+        return ACTION_NOP

+ 9
- 0
lab2/CustomBreadthFirstSearch.java View File

@@ -0,0 +1,9 @@
1
+package searchCustom;
2
+
3
+public class CustomBreadthFirstSearch  extends CustomGraphSearch{
4
+
5
+	public   CustomBreadthFirstSearch(int maxDepth){
6
+		super(false);
7
+		//System.out.println("Change line above in \"CustomBreadthFirstSearch.java\"! --> done.");
8
+	}
9
+};

+ 8
- 0
lab2/CustomDepthFirstSearch.java View File

@@ -0,0 +1,8 @@
1
+package searchCustom;
2
+
3
+public class CustomDepthFirstSearch extends CustomGraphSearch{
4
+	public CustomDepthFirstSearch(int maxDepth){
5
+		super(true);
6
+		//System.out.println("Change line above in \"CustomDepthFirstSearch.java\"! --> done.");
7
+	}
8
+};

+ 141
- 0
lab2/CustomGraphSearch.java View File

@@ -0,0 +1,141 @@
1
+package searchCustom;
2
+
3
+import java.util.ArrayList;
4
+import java.util.HashSet;
5
+
6
+import searchShared.NodeQueue;
7
+import searchShared.Problem;
8
+import searchShared.SearchObject;
9
+import searchShared.SearchNode;
10
+
11
+import world.GridPos;
12
+
13
+public class CustomGraphSearch implements SearchObject {
14
+
15
+	private HashSet<SearchNode> explored;
16
+	private NodeQueue frontier;
17
+	protected ArrayList<SearchNode> path;
18
+	private boolean insertFront;
19
+
20
+	/**
21
+	 * The constructor tells graph search whether it should insert nodes to front or back of the frontier 
22
+	 */
23
+    public CustomGraphSearch(boolean bInsertFront) {
24
+		insertFront = bInsertFront;
25
+    }
26
+
27
+	/**
28
+	 * Implements "graph search", which is the foundation of many search algorithms
29
+	 */
30
+	public ArrayList<SearchNode> search(Problem problem) {
31
+		// The frontier is a queue of expanded SearchNodes not processed yet
32
+		frontier = new NodeQueue();
33
+		// The explored set is a set of nodes that have been processed 
34
+		explored = new HashSet<SearchNode>();
35
+		// The start state is given
36
+		GridPos startState = (GridPos) problem.getInitialState();
37
+		// Initialise the frontier with the start state  
38
+		frontier.addNodeToFront(new SearchNode(startState));
39
+
40
+		// Path will be empty until we find the goal.
41
+		path = new ArrayList<SearchNode>();
42
+		
43
+		
44
+		/*
45
+		 * Note on how java and this program work:
46
+		 *  -ArrayList are just resizable array. "<>" contains the type of the listed objects
47
+		 *  -Structures :
48
+		 *  	while (boolean) {}
49
+		 *  	while (a > 0) {}
50
+		 *  	for (int i = 0; i < 5; i++){}		--> 5 iterations
51
+		 *  	for (String i : StringList){}		--> Iterations on all the objects in the list
52
+		 *  -Class used here:
53
+		 *  	-SearchNode					--> Equivalent of the "Node" class in the Python program. Represent a node.
54
+		 *  	-Problem 					--> Class containing lot of informations on the problem
55
+		 *  									like the reachable states from a position, the start, the end
56
+		 *  	-GridPos 					--> represent a position (with x and y coordinates)  	
57
+		 */
58
+		
59
+		
60
+		// While we have something to explore
61
+		while (frontier.size() > 0) {
62
+			// Get the first node on the list
63
+			SearchNode Node = frontier.removeFirst();
64
+			
65
+			// We check if it is our destination
66
+			if (problem.isGoalState(Node.getState())) {
67
+				return path = Node.getPathFromRoot();
68
+			} // if not, we continue
69
+			
70
+			// Get the list of children of the current node
71
+			ArrayList<GridPos> childPosition = problem.getReachableStatesFrom(Node.getState());
72
+			// For every child in the list
73
+			for (GridPos position : childPosition) {
74
+				// Creating a new node
75
+				SearchNode childNode = new SearchNode(position, Node);
76
+				// If we have not explored the node
77
+				if (explored.contains(childNode) == false) {
78
+					// Adding the child node to the frontier
79
+					// Front --> DFS (LIFO queue)
80
+					// Back --> BFS (FIFO queue)
81
+					if (insertFront) {
82
+						frontier.addNodeToFront(childNode);
83
+					}
84
+					else {
85
+						frontier.addNodeToBack(childNode);
86
+					}
87
+					// We add the node to explored node
88
+					explored.add(childNode);
89
+				}
90
+			}
91
+		}
92
+		
93
+		
94
+		/* Some hints:
95
+		 * -Read early part of chapter 3 in the book!
96
+		 * -You are free to change anything how you wish as long as the program runs, but some structure is given to help you.
97
+		 * -You can Google for "javadoc <class>" if you are uncertain of what you can do with a particular Java type.
98
+		 * 
99
+		 * -SearchNodes are the nodes of the search tree and contains the relevant problem state, in this case x,y position (GridPos) of the agent 
100
+		 * --You can create a new search node from a state by: SearchNode childNode = new SearchNode(childState, currentNode);
101
+		 * --You can also extract the state by .getState() method
102
+		 * --All search structures use search nodes, but the problem object only speaks in state, so you may need to convert between them 
103
+		 * 
104
+		 * -The frontier is a queue of search nodes, open this class to find out what you can do with it! 
105
+		 * 
106
+		 * -If you are unfamiliar with Java, the "HashSet<SearchNode>" used for the explored set means a set of SearchNode objects.
107
+		 * --You can add nodes to the explored set, or check if it contains a node!
108
+		 * 
109
+		 * -To get the child states (adjacent grid positions that are not walls) of a particular search node, do: ArrayList<GridPos> childStates = p.getReachableStatesFrom(currentState);
110
+		 * 
111
+		 * -Depending on the addNodesToFront boolean variable, you may need to do something with the frontier... (see book)
112
+		 * 
113
+		 * -You can check if you have reached the goal with p.isGoalState(NodeState)
114
+		 * 
115
+		 *  When the goal is found, the path to be returned can be found by: path = node.getPathFromRoot();
116
+		 */
117
+		/* Note: Returning an empty path signals that no path exists */
118
+		return path;
119
+	}
120
+
121
+	/*
122
+	 * Functions below are just getters used externally by the program 
123
+	 */
124
+	public ArrayList<SearchNode> getPath() {
125
+		return path;
126
+	}
127
+
128
+	public ArrayList<SearchNode> getFrontierNodes() {
129
+		return new ArrayList<SearchNode>(frontier.toList());
130
+	}
131
+	public ArrayList<SearchNode> getExploredNodes() {
132
+		return new ArrayList<SearchNode>(explored);
133
+	}
134
+	public ArrayList<SearchNode> getAllExpandedNodes() {
135
+		ArrayList<SearchNode> allNodes = new ArrayList<SearchNode>();
136
+		allNodes.addAll(getFrontierNodes());
137
+		allNodes.addAll(getExploredNodes());
138
+		return allNodes;
139
+	}
140
+
141
+}

+ 20
- 0
lab3/bayes.jnlp View File

@@ -0,0 +1,20 @@
1
+<?xml version="1.0" encoding="UTF-8"?>
2
+<jnlp spec="1.0+"
3
+  codebase="http://www.aispace.org/"
4
+>
5
+<information>
6
+  <title>Belief and Decision Networks</title>
7
+  <vendor>AIspace</vendor>
8
+  <homepage href="www.aispace.org" />
9
+</information>
10
+<offline-allowed/>
11
+<security>
12
+  <all-permissions/>
13
+</security>
14
+<resources>
15
+  <j2se version="1.5+" />
16
+  <jar href="bayes/bayes.jar"/>
17
+</resources>
18
+<application-desc />
19
+</jnlp>
20
+

+ 263
- 0
lab3/lab3_Mr_HS_v2.xml View File

@@ -0,0 +1,263 @@
1
+<?xml version="1.0" encoding="UTF-8"?>
2
+<BIF VERSION="0.3"  xmlns="http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3"
3
+	xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
4
+	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">
5
+<NETWORK>
6
+<NAME>Nuclear Power Station</NAME>
7
+<PROPERTY>detailed = </PROPERTY>
8
+<PROPERTY>short = </PROPERTY>
9
+
10
+<VARIABLE TYPE="nature">
11
+	<NAME>WaterLeak</NAME>
12
+	<OUTCOME>T</OUTCOME>
13
+	<OUTCOME>F</OUTCOME>
14
+	<PROPERTY>position = (7367.91845703125, 5357.0380859375)</PROPERTY>
15
+</VARIABLE>
16
+
17
+<VARIABLE TYPE="nature">
18
+	<NAME>WaterLeakWarning</NAME>
19
+	<OUTCOME>T</OUTCOME>
20
+	<OUTCOME>F</OUTCOME>
21
+	<PROPERTY>position = (7195.8505859375, 5497.32470703125)</PROPERTY>
22
+</VARIABLE>
23
+
24
+<VARIABLE TYPE="nature">
25
+	<NAME>IcyWeather</NAME>
26
+	<OUTCOME>T</OUTCOME>
27
+	<OUTCOME>F</OUTCOME>
28
+	<PROPERTY>position = (7565.81103515625, 5097.51025390625)</PROPERTY>
29
+</VARIABLE>
30
+
31
+<VARIABLE TYPE="nature">
32
+	<NAME>PumpFailure</NAME>
33
+	<OUTCOME>T</OUTCOME>
34
+	<OUTCOME>F</OUTCOME>
35
+	<PROPERTY>position = (7121.32080078125, 5349.66455078125)</PROPERTY>
36
+</VARIABLE>
37
+
38
+<VARIABLE TYPE="nature">
39
+	<NAME>PumpFailureWarning</NAME>
40
+	<OUTCOME>T</OUTCOME>
41
+	<OUTCOME>F</OUTCOME>
42
+	<PROPERTY>position = (6830.72802734375, 5504.23828125)</PROPERTY>
43
+</VARIABLE>
44
+
45
+<VARIABLE TYPE="nature">
46
+	<NAME>Meltdown</NAME>
47
+	<OUTCOME>T</OUTCOME>
48
+	<OUTCOME>F</OUTCOME>
49
+	<PROPERTY>position = (7467.087890625, 5603.3173828125)</PROPERTY>
50
+</VARIABLE>
51
+
52
+<VARIABLE TYPE="nature">
53
+	<NAME>Battery</NAME>
54
+	<OUTCOME>T</OUTCOME>
55
+	<OUTCOME>F</OUTCOME>
56
+	<PROPERTY>position = (7761.697265625, 5233.7236328125)</PROPERTY>
57
+</VARIABLE>
58
+
59
+<VARIABLE TYPE="nature">
60
+	<NAME>Radio</NAME>
61
+	<OUTCOME>T</OUTCOME>
62
+	<OUTCOME>F</OUTCOME>
63
+	<PROPERTY>position = (7667.05322265625, 5364.93359375)</PROPERTY>
64
+</VARIABLE>
65
+
66
+<VARIABLE TYPE="nature">
67
+	<NAME>Ignition</NAME>
68
+	<OUTCOME>T</OUTCOME>
69
+	<OUTCOME>F</OUTCOME>
70
+	<PROPERTY>position = (7914.4169921875, 5364.93359375)</PROPERTY>
71
+</VARIABLE>
72
+
73
+<VARIABLE TYPE="nature">
74
+	<NAME>Gas</NAME>
75
+	<OUTCOME>T</OUTCOME>
76
+	<OUTCOME>F</OUTCOME>
77
+	<PROPERTY>position = (8158.5546875, 5373.53759765625)</PROPERTY>
78
+</VARIABLE>
79
+
80
+<VARIABLE TYPE="nature">
81
+	<NAME>Start</NAME>
82
+	<OUTCOME>T</OUTCOME>
83
+	<OUTCOME>F</OUTCOME>
84
+	<PROPERTY>position = (8051.0048828125, 5523.03076171875)</PROPERTY>
85
+</VARIABLE>
86
+
87
+<VARIABLE TYPE="nature">
88
+	<NAME>Moves</NAME>
89
+	<OUTCOME>T</OUTCOME>
90
+	<OUTCOME>F</OUTCOME>
91
+	<PROPERTY>position = (8051.0048828125, 5678.9775390625)</PROPERTY>
92
+</VARIABLE>
93
+
94
+<VARIABLE TYPE="nature">
95
+	<NAME>Survives</NAME>
96
+	<OUTCOME>T</OUTCOME>
97
+	<OUTCOME>F</OUTCOME>
98
+	<PROPERTY>position = (7638.01513671875, 5822.0185546875)</PROPERTY>
99
+</VARIABLE>
100
+
101
+<VARIABLE TYPE="nature">
102
+	<NAME>Bicycle_works</NAME>
103
+	<OUTCOME>T</OUTCOME>
104
+	<OUTCOME>F</OUTCOME>
105
+	<PROPERTY>position = (7636.93896484375, 5681.12890625)</PROPERTY>
106
+</VARIABLE>
107
+
108
+<VARIABLE TYPE="nature">
109
+	<NAME>HS_is_awake</NAME>
110
+	<OUTCOME>T</OUTCOME>
111
+	<OUTCOME>F</OUTCOME>
112
+	<OBS>F</OBS>
113
+	<PROPERTY>position = (7004.9658203125, 5804.48974609375)</PROPERTY>
114
+</VARIABLE>
115
+
116
+<VARIABLE TYPE="nature">
117
+	<NAME>HS_do_something_not_stupid</NAME>
118
+	<OUTCOME>T</OUTCOME>
119
+	<OUTCOME>F</OUTCOME>
120
+	<PROPERTY>position = (7317.30908203125, 5806.892578125)</PROPERTY>
121
+</VARIABLE>
122
+
123
+<VARIABLE TYPE="nature">
124
+	<NAME>HS_remark_irregularity</NAME>
125
+	<OUTCOME>T</OUTCOME>
126
+	<OUTCOME>F</OUTCOME>
127
+	<PROPERTY>position = (7008.78662109375, 5680.53564453125)</PROPERTY>
128
+</VARIABLE>
129
+
130
+<VARIABLE TYPE="nature">
131
+	<NAME>Pay_day</NAME>
132
+	<OUTCOME>T</OUTCOME>
133
+	<OUTCOME>F</OUTCOME>
134
+	<PROPERTY>position = (6790.44482421875, 5682.78271484375)</PROPERTY>
135
+</VARIABLE>
136
+
137
+<VARIABLE TYPE="nature">
138
+	<NAME>Warning(s)?</NAME>
139
+	<OUTCOME>T</OUTCOME>
140
+	<OUTCOME>F</OUTCOME>
141
+	<OBS>T</OBS>
142
+	<PROPERTY>position = (7014.93408203125, 5581.3759765625)</PROPERTY>
143
+</VARIABLE>
144
+
145
+<DEFINITION>
146
+	<FOR>WaterLeak</FOR>
147
+	<GIVEN>IcyWeather</GIVEN>
148
+	<TABLE>0.2 0.8 0.1 0.9</TABLE>
149
+</DEFINITION>
150
+
151
+<DEFINITION>
152
+	<FOR>WaterLeakWarning</FOR>
153
+	<GIVEN>WaterLeak</GIVEN>
154
+	<TABLE>0.9 0.1 0.05 0.95</TABLE>
155
+</DEFINITION>
156
+
157
+<DEFINITION>
158
+	<FOR>IcyWeather</FOR>
159
+	<TABLE>0.05 0.95</TABLE>
160
+</DEFINITION>
161
+
162
+<DEFINITION>
163
+	<FOR>PumpFailure</FOR>
164
+	<TABLE>0.1 0.9</TABLE>
165
+</DEFINITION>
166
+
167
+<DEFINITION>
168
+	<FOR>PumpFailureWarning</FOR>
169
+	<GIVEN>PumpFailure</GIVEN>
170
+	<TABLE>0.9 0.1 0.05 0.95</TABLE>
171
+</DEFINITION>
172
+
173
+<DEFINITION>
174
+	<FOR>Meltdown</FOR>
175
+	<GIVEN>WaterLeak</GIVEN>
176
+	<GIVEN>PumpFailure</GIVEN>
177
+	<GIVEN>HS_do_something_not_stupid</GIVEN>
178
+	<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>
179
+</DEFINITION>
180
+
181
+<DEFINITION>
182
+	<FOR>Battery</FOR>
183
+	<GIVEN>IcyWeather</GIVEN>
184
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186
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187
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188
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189
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190
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198
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199
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201
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202
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203
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205
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206
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209
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216
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217
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219
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221
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239
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+	<GIVEN>PumpFailureWarning</GIVEN>
247
+	<GIVEN>HS_is_awake</GIVEN>
248
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250
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251
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252
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253
+	<TABLE>0.03 0.97</TABLE>
254
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255
+
256
+<DEFINITION>
257
+	<FOR>Warning(s)?</FOR>
258
+	<GIVEN>WaterLeakWarning</GIVEN>
259
+	<GIVEN>PumpFailureWarning</GIVEN>
260
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262
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263
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+ 192
- 0
lab3/lab3_bicycle.xml View File

@@ -0,0 +1,192 @@
1
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+<VARIABLE TYPE="nature">
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+	<NAME>PumpFailure</NAME>
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+<VARIABLE TYPE="nature">
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+	<NAME>PumpFailureWarning</NAME>
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+
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+<VARIABLE TYPE="nature">
46
+	<NAME>Meltdown</NAME>
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+	<OUTCOME>T</OUTCOME>
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145
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161
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+	<FOR>Start</FOR>
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+	<GIVEN>Start</GIVEN>
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+	<TABLE>0.95 0.05 0.0 1.0</TABLE>
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+</DEFINITION>
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+
179
+<DEFINITION>
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+	<FOR>Survives</FOR>
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+	<GIVEN>Meltdown</GIVEN>
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+	<GIVEN>Moves</GIVEN>
183
+	<GIVEN>Bicycle_works</GIVEN>
184
+	<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>
185
+</DEFINITION>
186
+
187
+<DEFINITION>
188
+	<FOR>Bicycle_works</FOR>
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+	<TABLE>0.9 0.1</TABLE>
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+</DEFINITION>
191
+</NETWORK>
192
+</BIF>

+ 179
- 0
lab3/lab3_new_car.xml View File

@@ -0,0 +1,179 @@
1
+<?xml version="1.0" encoding="UTF-8"?>
2
+<BIF VERSION="0.3"  xmlns="http://www.cs.ubc.ca/labs/lci/fopi/ve/XMLBIFv0_3"
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+	xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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+<VARIABLE TYPE="nature">
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+	<NAME>WaterLeak</NAME>
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+	<OUTCOME>T</OUTCOME>
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+	<OUTCOME>F</OUTCOME>
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+</VARIABLE>
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+<VARIABLE TYPE="nature">
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+	<NAME>WaterLeakWarning</NAME>
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+<VARIABLE TYPE="nature">
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+	<NAME>IcyWeather</NAME>
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+<VARIABLE TYPE="nature">
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+	<NAME>PumpFailure</NAME>
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+	<OUTCOME>T</OUTCOME>
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+<VARIABLE TYPE="nature">
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+	<NAME>Meltdown</NAME>
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127
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128
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129
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130
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131
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132
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133
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134
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135
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136
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137
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138
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139
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141
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142
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143
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144
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145
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146
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147
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148
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149
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150
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151
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152
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153
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155
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156
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157
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163
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165
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+ 88
- 0
lab3/nuclear_power_station.xml View File

@@ -0,0 +1,88 @@
1
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+   <OUTCOME>F</OUTCOME>
40
+   <PROPERTY>position = (-203.0, 47.0)</PROPERTY>
41
+</VARIABLE>
42
+
43
+<VARIABLE TYPE="nature">
44
+   <NAME>Meltdown</NAME>
45
+   <OUTCOME>T</OUTCOME>
46
+   <OUTCOME>F</OUTCOME>
47
+   <PROPERTY>position = (-60.0, 140.0)</PROPERTY>
48
+</VARIABLE>
49
+
50
+
51
+<DEFINITION>
52
+   <FOR>WaterLeak</FOR>
53
+   <GIVEN>IcyWeather</GIVEN>
54
+   <TABLE> 0.2 0.8 0.1 0.9</TABLE>
55
+</DEFINITION>
56
+
57
+<DEFINITION>
58
+   <FOR>WaterLeakWarning</FOR>
59
+   <GIVEN>WaterLeak</GIVEN>
60
+   <TABLE> 0.9 0.1 0.05 0.95</TABLE>
61
+</DEFINITION>
62
+
63
+<DEFINITION>
64
+   <FOR>IcyWeather</FOR>
65
+   <TABLE> 0.05 0.95</TABLE>
66
+</DEFINITION>
67
+
68
+<DEFINITION>
69
+   <FOR>PumpFailure</FOR>
70
+   <TABLE> 0.1 0.9</TABLE>
71
+</DEFINITION>
72
+
73
+<DEFINITION>
74
+   <FOR>PumpFailureWarning</FOR>
75
+   <GIVEN>PumpFailure</GIVEN>
76
+   <TABLE> 0.9 0.1 0.05 0.95</TABLE>
77
+</DEFINITION>
78
+
79
+<DEFINITION>
80
+   <FOR>Meltdown</FOR>
81
+   <GIVEN>PumpFailure</GIVEN>
82
+   <GIVEN>WaterLeak</GIVEN>
83
+   <TABLE> 0.2 0.8 0.15 0.85 0.1 0.9 0.0010 0.999</TABLE>
84
+</DEFINITION>
85
+
86
+
87
+</NETWORK>
88
+</BIF>

+ 63
- 0
lab4/part1/results.md View File

@@ -0,0 +1,63 @@
1
+## Some results for pb1 on domain "Shakey"
2
+The problem 1 is not complex enough to have time comparison, but we can observe different strategies.
3
+
4
+### IPP algorithm
5
+```
6
+time step    0: MOVE R2 R1
7
+time step    1: PUSH BOX1 R1 R2
8
+time step    2: PUSH BOX1 R2 R3
9
+time step    3: TURN_ON_LIGHT R3 BOX1
10
+time step    4: GRAB SMALL4 R3 LEFT
11
+                GRAB SMALL3 R3 RIGHT
12
+time step    5: PUSH BOX1 R3 R2
13
+time step    6: PUSH BOX1 R2 R1
14
+time step    7: RELEASE SMALL3 R1 RIGHT
15
+                RELEASE SMALL4 R1 LEFT
16
+time step    8: PUSH BOX1 R1 R2
17
+time step    9: TURN_ON_LIGHT R2 BOX1
18
+time step   10: GRAB SMALL2 R2 RIGHT
19
+time step   11: PUSH BOX1 R2 R1
20
+time step   12: RELEASE SMALL2 R1 RIGHT
21
+
22
+Memory used:   0.09 MBytes for domain representation
23
+               2.50 MBytes for graph
24
+               0.04 MBytes for exclusions
25
+               0.04 MBytes for memoization
26
+               0.14 MBytes for wave front
27
+```
28
+### FF algorithm
29
+```
30
+step    0: MOVE R2 R1
31
+        1: PUSH BOX1 R1 R2
32
+        2: TURN_ON_LIGHT R2 BOX1
33
+        3: GRAB SMALL2 R2 LEFT
34
+        4: MOVE R2 R1
35
+        5: RELEASE SMALL2 R1 LEFT
36
+        6: MOVE R1 R2
37
+        7: PUSH BOX1 R2 R3
38
+        8: TURN_ON_LIGHT R3 BOX1
39
+        9: GRAB SMALL3 R3 LEFT
40
+       10: GRAB SMALL4 R3 RIGHT
41
+       11: MOVE R3 R2
42
+       12: MOVE R2 R1
43
+       13: RELEASE SMALL3 R1 LEFT
44
+       14: RELEASE SMALL4 R1 RIGHT
45
+```
46
+### LAMA algorithm
47
+```
48
+(move r2 r1)
49
+(push box1 r1 r2)
50
+(turn_on_light r2 box1)
51
+(grab small2 r2 right)
52
+(move r2 r1)
53
+(release small2 r1 right)
54
+(move r1 r2)
55
+(push box1 r2 r3)
56
+(turn_on_light r3 box1)
57
+(grab small3 r3 left)
58
+(grab small4 r3 right)
59
+(move r3 r2)
60
+(move r2 r1)
61
+(release small3 r1 left)
62
+(release small4 r1 right)
63
+```

+ 73
- 0
lab4/part1/task1_domain.pddl View File

@@ -0,0 +1,73 @@
1
+;; TDDC17 - Lab 4
2
+;; Shakey's World
3
+
4
+;; The objective here is to give a basic
5
+;; domain for the Shakey's World problem.
6
+
7
+;; Our domain definition
8
+(define (domain shakey)
9
+	;; We only use strips
10
+	(:requirements :strips)	
11
+	
12
+	(:types 
13
+		room	;; Represent a room
14
+		object	;; Represent any object (small objects or boxes)
15
+		gripper ;; Represent Shakey's gripper
16
+	)	
17
+
18
+	;; Predicates definition
19
+	(:predicates
20
+		(position ?r - room)			;; Is Shakey in the given room?
21
+		(light ?r - room)			;; Are lights on in the given room?
22
+
23
+		(connected_wide ?r1 ?r2 - room)		;; Are rooms connected by a wide door?
24
+		(connected ?r1 ?r2 - room)		;; Are rooms connected by a door?
25
+		
26
+		(in ?o - object ?r - room)		;; Is the given object in the given room?
27
+		(is_empty ?g - gripper)			;; Is the given gripper empty?
28
+		(hold_on ?g - gripper ?o - object)	;; Is the given gripper holding the given object?
29
+		(is_big ?o - object)			;; Is this object big? I.e. is this object a box?
30
+	)
31
+
32
+	;; Actions definition
33
+	;; Robot's actions
34
+	(:action move		;; MOVE action, from the room ?pos to ?trg
35
+		:parameters	(?pos ?trg - room)
36
+		:precondition	(and (position ?pos) 
37
+				(connected ?pos ?trg))
38
+		:effect		(and (not (position ?pos)) (position ?trg))
39
+	)
40
+	(:action turn_on_light	;; Act of turning on lights. Requires a support.
41
+		:parameters	(?pos - room ?support - object)
42
+		:precondition   (and (position ?pos)
43
+				     (in ?support ?pos)
44
+				     (is_big ?support)
45
+				     (not (light ?pos)))
46
+		:effect		(light ?pos)
47
+	)
48
+
49
+	;; Object actions
50
+	(:action grab		;; Action to GRAB sth
51
+		:parameters	(?what - object ?where - room ?g - gripper)
52
+		:precondition	(and (position ?where)
53
+				     (light ?where)
54
+				     (in ?what ?where)
55
+				     (is_empty ?g)
56
+				     (not (is_big ?what)))
57
+		:effect		(and (not (is_empty ?g))(hold_on ?g ?what)(in ?what ?where))
58
+	)
59
+	(:action release	;; Action to RELEASE sth
60
+		:parameters	(?what - object ?where - room ?g - gripper)
61
+		:precondition   (and (position ?where)
62
+				     (hold_on ?g ?what))
63
+		:effect		(and (not (hold_on ?g ?what))(in ?what ?where)(is_empty ?g))
64
+	)
65
+	(:action push		;; Action to PUSH sth
66
+		:parameters	(?what - object ?r1 - room ?r2 - room)
67
+		:precondition   (and (position ?r1)
68
+				     (is_big ?what)
69
+				     (in ?what ?r1)
70
+				     (connected_wide ?r1 ?r2))
71
+		:effect		(and (position ?r2)(not (position ?r1))(in ?what ?r2)(not(in ?what ?r1)))
72
+	)
73
+)	

+ 89
- 0
lab4/part1/task1_pb1.pddl View File

@@ -0,0 +1,89 @@
1
+;; TDDC17 - Lab 4
2
+;; S.'s W. problem 1
3
+;; Last modification: 2020-10-04
4
+
5
+;; Problem description
6
+;; The first problem has three rooms, connect by doors as discribed below:
7
+;;      -------------------------------------------------------------------------
8
+;;      |                       |                       |                       |
9
+;;      |                       |                       |                       |
10
+;;      |       light switch 1 -|- light switch2        |- light switch3        |
11
+;;      |                       |                       |                       |
12
+;;      |       ---             |                     door2                     |
13
+;;      |       | |           door1      shakey         |                       |
14
+;;      |       ---           (wide)                    |                       |
15
+;;      |       box             |                       |                       |
16
+;;      |                       |                     door3                     |
17
+;;      |                       |                     (wide)                    |
18
+;;      |        r1             |        r2             |         r3            |
19
+;;      -------------------------------------------------------------------------
20
+;; Shakey must find four small objects distributed in the three rooms and bring 
21
+;; them back to the first room. All lights are off initially. The box allowing 
22
+;; Shakey to turn on the lights is in the first room.
23
+
24
+
25
+;; Our problem1 definition
26
+(define (problem pb1)
27
+	(:domain shakey)
28
+	
29
+	(:objects
30
+		;; Our three rooms
31
+		r1 - room
32
+		r2 - room
33
+		r3 - room
34
+
35
+		;; The box
36
+		box1 - object
37
+
38
+		;; The four small objects
39
+		small1 - object
40
+		small2 - object
41
+		small3 - object
42
+		small4 - object
43
+
44
+		;; The two Shakey's grippers
45
+		right - gripper
46
+		left - gripper
47
+	)
48
+	
49
+	(:init
50
+		;; Init position
51
+		;; At the beginning, Shakey is in room 2.
52
+		(position r2)
53
+		
54
+		;; Init connections (as discribed above).
55
+		(connected_wide r1 r2)
56
+		(connected_wide r2 r1)
57
+		(connected_wide r2 r3)
58
+		(connected_wide r3 r2)
59
+		(connected r1 r2)
60
+		(connected r2 r1)
61
+		(connected r2 r3)
62
+		(connected r3 r2)
63
+
64
+		;; Init object positions
65
+		(in box1 r1)
66
+		(is_big box1)
67
+		(in small1 r1)
68
+		(in small2 r2)
69
+		(in small3 r3)
70
+		(in small4 r3)
71
+		
72
+		;; Init light (light nowhere)
73
+		;;(not (light r1))
74
+		;;(not (light r2))
75
+		;;(not (light r3))
76
+
77
+		;; Init gripper
78
+		(is_empty right)
79
+		(is_empty left)
80
+	)
81
+	
82
+	;; The goal is to have all four objects in the room 1.
83
+	(:goal
84
+		(and (in small1 r1)
85
+		(in small2 r1)
86
+                (in small3 r1)
87
+                (in small4 r1))
88
+	)
89
+)

+ 68
- 0
lab4/part2/domain.pddl View File

@@ -0,0 +1,68 @@
1
+(define (domain transport-strips)
2
+(:requirements :typing :action-costs)
3
+
4
+(:types location fuellevel locatable - object 
5
+	package truck - locatable
6
+)
7
+
8
+(:predicates 
9
+(connected ?l1 ?l2 - location)
10
+(at ?o - locatable ?l - location)
11
+(in ?p - package ?t - truck)
12
+(fuel ?t - truck ?level - fuellevel)
13
+(fuelcost ?level - fuellevel ?l1 ?l2 - location)
14
+(sum ?a ?b ?c - fuellevel)
15
+)
16
+
17
+(:functions 
18
+(total-cost) - number)
19
+
20
+(:action LOAD
21
+:parameters
22
+(?p - package
23
+?t - truck
24
+?l - location)
25
+:precondition
26
+(and (at ?t ?l) (at ?p ?l))
27
+:effect
28
+(and (not (at ?p ?l)) (in ?p ?t) (increase (total-cost) 1))
29
+)
30
+
31
+(:action UNLOAD
32
+:parameters
33
+(?p - package
34
+?t - truck
35
+?l - location)
36
+:precondition
37
+(and (at ?t ?l) (in ?p ?t))
38
+:effect
39
+(and (at ?p ?l) (not (in ?p ?t)) (increase (total-cost) 1))
40
+)
41
+
42
+(:action DRIVE
43
+:parameters
44
+(?t - truck
45
+?l1 - location
46
+?l2 - location
47
+?fuelpost - fuellevel
48
+?fueldelta - fuellevel
49
+?fuelpre - fuellevel)
50
+:precondition
51
+(and 
52
+(connected ?l1 ?l2)
53
+(fuelcost ?fueldelta ?l1 ?l2)
54
+(fuel ?t ?fuelpre)
55
+(sum ?fuelpost ?fueldelta ?fuelpre)
56
+(at ?t ?l1)
57
+)
58
+:effect
59
+(and (not (at ?t ?l1)) 
60
+     (at ?t ?l2) 
61
+     (not (fuel ?t ?fuelpre)) 
62
+     (fuel ?t ?fuelpost)
63
+     (increase (total-cost) 1))
64
+)
65
+
66
+)
67
+
68
+

+ 27
- 0
lab4/part2/exe.sh View File

@@ -0,0 +1,27 @@
1
+#!/bin/bash
2
+# TDDC17 
3
+# Script to run all problems with all configurations
4
+
5
+echo "Setting up env."
6
+mkdir output
7
+echo -ne "Working in: "
8
+pwd
9
+echo ""
10
+
11
+## CONFIG 1: FF heuristic
12
+echo "$i: Running configuration 1 (Eager greedy search, FF heuristic)"
13
+/courses/TDDC17/sw/fdlog/fast-downward.py domain.pddl p02.pddl --log-file output/output-config1FF-pb2 --heuristic 'hff=ff()' --search 'eager_greedy([hff])'
14
+/courses/TDDC17/sw/fdlog/fast-downward.py domain.pddl p03.pddl --log-file output/output-config1FF-pb3 --heuristic 'hff=ff()' --search 'eager_greedy([hff])'
15
+
16
+## CONFIG 2: Goal count heristic
17
+echo "$i: Running configuration 2 (Eager greedy search, Goal count heuristic)"
18
+/courses/TDDC17/sw/fdlog/fast-downward.py domain.pddl p02.pddl --log-file output/output-config2GC-pb2 --heuristic 'gc=goalcount()' --search 'eager_greedy([gc])'
19
+/courses/TDDC17/sw/fdlog/fast-downward.py domain.pddl p03.pddl --log-file output/output-config2GC-pb3 --heuristic 'gc=goalcount()' --search 'eager_greedy([gc])'
20
+
21
+## CONFIG 3: Goal count heuristic with FF helpful actions
22
+echo "$i: Running configuration 2 (Eager greedy search, goal count heuristic with FF helpful actions)"
23
+/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)'
24
+/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)'
25
+
26
+echo "Done."
27
+exit 0

+ 1421
- 0
lab4/part2/output/output-config1ff-pb2.txt
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+ 48394
- 0
lab4/part2/output/output-config1ff-pb3.txt
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+ 269167
- 0
lab4/part2/output/output-config2gc-pb2.txt
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+ 10366
- 0
lab4/part2/output/output-config2gc-pb3.txt
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+ 52447
- 0
lab4/part2/output/output-config3gf-pb2.txt
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+ 8976
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lab4/part2/output/output-config3gf-pb3.txt
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+ 699
- 0
lab4/part2/output_shakey/output-config1ff-shakey.txt View File

@@ -0,0 +1,699 @@
1
+{"act": "begin_group", "ts": 0}
2
+{"act": "open_list_sorting", "key_list": ["h_SINGLE", "h_(", "h_ff", "h_INSERTION_ORDER", "h_)"]}
3
+{"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}
4
+{"ts": 0, "act": "evaluated_states", "state_id": 0, "preferred": 1}
5
+{"act": "end_group"}
6
+{"act": "begin_group", "ts": 0}
7
+{"ts":0, "act": "state_update", "state_id": 0, "h_ff": 12}
8
+{"ts": 0, "act": "evaluated_heuristics", "count": 1}
9
+{"act": "preferred_heuristics", "key_list": []}
10
+{"act": "main_heuristic", "target": "h_ff"}
11
+{"act": "end_group"}
12
+{"act": "begin_group", "ts": 1}
13
+{"ts":1, "act": "state_update", "state_id": 0, "g": 0, "real_g": 0}
14
+{"ts": 1, "act": "expanded_states", "state_id": 0}
15
+{"act": "end_group"}
16
+{"act": "begin_group", "ts": 1}
17
+{"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]}
18
+{"ts": 1, "act": "generated_states", "state_id": 0, "op": "move r2 r1"}
19
+{"ts":1, "act": "state_update", "state_id": 1, "g": -1, "real_g": -1}
20
+{"ts": 1, "act": "evaluated_states", "state_id": 1, "preferred": 0}
21
+{"act": "end_group"}
22
+{"act": "begin_group", "ts": 1}
23
+{"ts":1, "act": "state_update", "state_id": 1, "h_ff": 12}
24
+{"ts": 1, "act": "evaluated_heuristics", "count": 1}
25
+{"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]}
26
+{"ts": 1, "act": "generated_states", "state_id": 0, "op": "move r2 r3"}
27
+{"ts":1, "act": "state_update", "state_id": 2, "g": -1, "real_g": -1}
28
+{"ts": 1, "act": "evaluated_states", "state_id": 2, "preferred": 0}
29
+{"act": "end_group"}
30
+{"act": "begin_group", "ts": 1}
31
+{"ts":1, "act": "state_update", "state_id": 2, "h_ff": 12}
32
+{"ts": 1, "act": "evaluated_heuristics", "count": 1}
33
+{"act": "end_group"}
34
+{"act": "begin_group", "ts": 2}
35
+{"ts":2, "act": "state_update", "state_id": 1, "g": 1, "real_g": 1}
36
+{"ts": 2, "act": "expanded_states", "state_id": 1}
37
+{"act": "end_group"}
38
+{"act": "begin_group", "ts": 2}
39
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