Ropossum is a framework that lets you play the beloved Cut The Rope game as much as you want and the levels will keep coming. You can design your own levels, check your designed levels for playability at real time, ask it to complete your unfinished designs according to your own preferences, or even suggest endless playable design variations according to your initial level design.

4. Presentation Timeline
•The problem, the solution and what others are doing
•The game and the research
•Ropossum: the framework
–Content generation
–Playability
–Authoring tool
•Results and analysis
•Future Perspectives

5. Presentation Timeline
•The problem, the solution and what others are doing
•The game and the research
•Ropossum: the framework
–Content generation
–Playability
–Authoring tool
•Results and analysis
•Future Perspectives

6. $93 Billion in 2013
Gaming Industry

7. Battlefield 4

8. What is wrong with games now?
Designer
Level design
Players

9. What is wrong with games now?
Designer
Level design
Gender?

10. What is wrong with games now?
Designer
Level design
Personalities?

11. What is wrong with games now?
Designer
Level design
NEEDS

12. What is wrong with games now?
Designer
Level design
What if I want
to play More?

13. What is wrong with games now?
Designer
Level design
What if I want
to play More?

14. What is wrong with games now?
Generator
Level design
What if I want
to play More?

15. Procedural
Content
Generation
(PCG)

16. 241,920,000 logins per month
4000 logins per second after 1.0 launch
2,000,000,000 files download by the launcher
11,000 skin downloads (in game) per second

17. No Man's Sky

18. Presentation Timeline
•The problem, the solution and what others are doing
•The game and the research
•Ropossum: the framework
–Content generation
–Playability
–Authoring tool
•Results and analysis
•Future Perspectives

19. -Popular physics based game
-Can generate endless levels
-All levels should be playable
-Opens the imagination of all players to design, test, modifytheir own levels and helpthem achieve that.
?

21. Cut the Rope
Sequels 2010, 2011, 2012 and 2013
WWDC 2011,won anApple Design Awardfor the iOSplatform
100 million times
1.6 million users/daily

22. The Game, Demo

23. This Work Publications, So Far..

24. “Automatic Generation and Analysis of Physics-Based Puzzle Games”
IEEE Conference on Computational Intelligence and Games, CIG 2013. Nominated for best paper award.
CIG 13
“Evolving Playable Content for Cut the Rope through a Simulation-Based Approach”
AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2013
AIIDE 13
“Ropossum: An Authoring Tool for Designing, optimizing and Solving Cut the Rope Levels”
AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2013
AIIDE 13

25. Presentation Timeline
•The problem, the solution and what others are doing
•The game and the research
•Ropossum: the framework
–Content generation
–Playability
–Authoring tool
•Results and analysis
•Future Perspectives

26. www.mohammadshaker.com/crust.html

27. Game Components

29. GE Context Free Grammars
<level>::=<candy><Om_Nom><components> <candy>::=candy(<x>,<y>) <Om_Nom>::=Om_Nom(<x>,<y>) <components>::=<rope><air_cush><bumper><rocket><bubble><more_components> <more_components>::=<component>|<component><more_components> <component>::=<rope>|<air_cush>|<rocket>|<bumper>|<bubble> <rope>::=rope(<x>,<y>,<rope_length>) <rocket>::=rocket(<x>,<y>,<rocket_dir>) <air_cush>::=air_cush(<x>,<y>,<air_cush_dir>) <bumper>::=bumper(<x>,<y>,<bumper_dir>) <bubble>::=bubble(<x>,<y>) <x>::=[0, 260] <y>::=[0, 420] <rope_length>::=[0, 170] <air_cush_dir>::= 0 | 1<bumper_dir>::=[0, 7] <rocket_dir>::=[0, 7]

30. GE Context Free Grammars
<level>::=<candy><Om_Nom><components> <candy>::=candy(<x>,<y>) <Om_Nom>::=Om_Nom(<x>,<y>) <components>::=<rope><air_cush><bumper><rocket><bubble><more_components> <more_components>::=<component>|<component><more_components> <component>::=<rope>|<air_cush>|<rocket>|<bumper>|<bubble> <rope>::=rope(<x>,<y>,<rope_length>) <rocket>::=rocket(<x>,<y>,<rocket_dir>) <air_cush>::=air_cush(<x>,<y>,<air_cush_dir>) <bumper>::=bumper(<x>,<y>,<bumper_dir>) <bubble>::=bubble(<x>,<y>) <x>::=[0, 260] <y>::=[0, 420] <rope_length>::=[0, 170] <air_cush_dir>::= 0 | 1<bumper_dir>::=[0, 7] <rocket_dir>::=[0, 7]

31. should be placed higher than Om Nom when the level contains no component to elevate the candy, such as bubbles or water.
Candy
should be placed under the closest rope when there is no water,
bubbles or rockets.
Om Nom
should be placed close to the end of a rope, inside a predefined circle surrounding the end of it.
Blower
Should be placed in a position where it does intersect with at least one of the ropes.
Bubble
rockets should aim at Om Nom
Rocket
Components shouldn’t intersect, 3 block size apart
Comps
25%
10%
10%
10%
20%
25%

32. =25∗푃푐푎푛푑푦+10∗푃푂푚푁표푚+10∗푁푏푙표푤푒푟∗푃푏푙표푤푒푟
+20∗푁푟표푐푘푒푡∗푂푟표푐푘푒푡+10∗푁푏푢푏푏푙푒∗푃푏푢푏푏푙푒+25∗퐶_표푣푒푟푙푎푝
Fitness

33. candy(481, 143) OmNom(287, 384) rope(445, 69, 90) rope(596, 100, 150) bubble(294, 76) rocket(471, 342, 5)

34. PCGEngine
Generate Level (Grammatical Evolution)
PCG with CRUST Generator Model

35. PCGEngine
Generate Level (Grammatical Evolution)
Fitness Evaluators
PCG with CRUST Generator Model

36. PCGEngine
CRUSTEngine
Generate Level (Grammatical Evolution)
Fitness Evaluators
Level Generator
Resting Phase
Visualize Level and Update
PCG with CRUST Generator Model

37. Generated Samples

38. “Automatic Generation and Analysis of Physics-Based Puzzle Games”
IEEE Conference on Computational Intelligence and Games, CIG 2013

39. Presentation Timeline
•The problem, the solution and what others are doing
•The game and the research
•Ropossum: the framework
–Content generation
–Playability: The Model
–Authoring tool
•Results and analysis
•Future Perspectives

40. Playability

41. Player
Actions
CRUSTworks on fixed time step 60 times per second
Rope cut
Blower press
Bubble burst
Rocket press
Void

42. If the game is 5 sec long, then we have 5*60 = 300 time step
Given we have 5 actions, then we need to evaluate

43. Shrink the SEARCH space with constrained Search with A Rule-based Agent

44. candy(497, 153).
velocity(0, 0).
OmNom(317, 414).
rope(0, 445, 69, 105).
rope(1, 596, 100, 165).
bubble_void(319, 101).
rocket_void(501, 357, 5).

45. Constraint search rule sets
[1] rope_cut :-rope(_,_), Om_Nom(Xo,Yo),
((candy(Xc,Yc), distance(Xo,Yo,Xc,Yc,50))| (active_bubble(_,Yb),(Yf<Yb))) [2] air-cush_press:-air_cush(X,Y,Dir), candy(Xc, Yc),
distance(X, Y, Xc, Yc, 80), candy_in_direction_of_air_cush(X,Y) [3] bubble_burst:-active_bubble(X,Y), OmNom(Xo, Yo), (Yo> Y),
distance(X, Y, 50),
((velocity_to_right, OmNom_to_right(X, Y)) |(velocity_to_left, OmNom_to_left(X, Y))) [4] rocket_press:-active_rocket(X,Y,_), Yo< Y, OmNom(Xo, Yo), distance(Xo,Yo,X,Y,100) [6] candy_in_direction_of_air_cush(X,Y) :-candy(Xc,Yc), air_cushion(X,Y,Dir), ((Xc< X , dir_to_left(Dir)) [5] void_action

46. [1] rope_cut :-rope(_,_), reachable(_)
[2] air-cush_press:-air-cush(X,Y,Dir), candy_in_direction_of_air-cush(X,Y), distance(X, Y, 80)
[3] bubble_pinch:-active_bubble(_,_), reachable(_)
[4] rocket_press:-active_rocket(X,Y,Dir), reachable(_)
[5] void_action :-rope_cut| bubble_pinch| air-cush_press| rocket_press| reachable(_)
Constraint search rule sets
[1] rope_cut :-rope(_,_), Om_Nom(Xo,Yo),
((candy(Xc,Yc), distance(Xo,Yo,Xc,Yc,50))| (active_bubble(_,Yb),(Yf<Yb))) [2] air-cush_press:-air_cush(X,Y,Dir), candy(Xc, Yc),
distance(X, Y, Xc, Yc, 80), candy_in_direction_of_air_cush(X,Y) [3] bubble_burst:-active_bubble(X,Y), OmNom(Xo, Yo), (Yo> Y),
distance(X, Y, 50),
((velocity_to_right, OmNom_to_right(X, Y)) |(velocity_to_left, OmNom_to_left(X, Y))) [4] rocket_press:-active_rocket(X,Y,_), Yo< Y, OmNom(Xo, Yo), distance(Xo,Yo,X,Y,100) [6] candy_in_direction_of_air_cush(X,Y) :-candy(Xc,Yc), air_cushion(X,Y,Dir), ((Xc< X , dir_to_left(Dir)) [5] void_action

47. Explore each action sub tree
CRUST Engine 2.0
Prolog Engine
Engine State
as Facts
Set of ordered potential actions
Actions tree branch
New
Engine State
Inference Engine
Playability Model Closed Loop

48. Demo

49. rope_cut(0)
rope_cut(1)
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
rocket_press
void_action

50. rope_cut(0)
rope_cut(1)
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
rocket_press
void_action

51. Presentation Timeline
•The problem, the solution and what others are doing
•The game and the research
•Ropossum: the framework
–Content generation
–Playability: The Performance
–Authoring tool
•Results and analysis
•Future Perspectives

52. Performance Enhancement Techniques
•Adaptive Time Step:
–blower_Press15
–rope_cut10
–void_action12
–bubble_burst15
–rocket_press17

53. rope_cut(0)
rope_cut(1)
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
rocket_press
void_action
9
10
11
12
13
14
15
16
17
18
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

54. Performance Enhancement Techniques
•Adaptive Time Step:
–blower_Press15
–rope_cut10
–void_action12
–bubble_burst15
–rocket_press17
•Branch Cut:
–Tree branch cut when trajectory doesn’t make sense

55. rope_cut(0)
rope_cut(1)
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
void_action
rocket_press
void_action

56. Demo. Only 215 explored nodes

57. Solution Tree
Only 205 nodes

58. Presentation Timeline
•The problem, the solution and what others are doing
•Ropossum; the game and the research
•Ropossum; the framework
–Content Generation
–Playability
•The playability model
•Performance enhancement
•Content generation with playability check
•Other playability models
–Authoring tool
•Results and analysis
•Future Perspectives

59. PCGEngine
CRUSTEngine
Generate Level (Grammatical Evolution)
fitness = 0.25 ∗design + 0.75 ∗playability_agent
Level Generator
Resting Phase
Visualize Level and Update
Content generation with playability check

60. “Evolving Playable Content for Cut the Rope through a Simulation-Based Approach”
AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2013

61. Presentation Timeline
•The problem, the solution and what others are doing
•The game and the research
•Ropossum: the framework
–Content generation
–Playability: Other Models?
–Authoring tool
•Results and analysis
•Future Perspectives

62. Output
Input
Previous State
StateHidden
Weights V
Weights U
Weights W
SRN
Output
Input
Previous State
State/ Hidden
Weights V
Weights U
Copy
(Delayed)
Weights W
Elman
Output
Input
Previous State
State/ Hidden
Weights V
Weights U
Weights W
Copy
(Delayed)
Jordan
Recurrent
Neural
Networks

63. Output
Input
Previous State
Hidden
Weights V
Weights U
State
Weights W
Standard Recurrent Network (SRN)

64. Output
Input
Previous State
State/ Hidden
Weights V
Weights U
Weights W
Elman RNN

65. Output
Input
Previous State
State/ Hidden
Weights V
Weights U
Copy
(Delayed)
Weights W
Elman RNN

66. Output
Input
Previous State
State/ Hidden
Weights V
Weights U
Copy
(Delayed)
Weights W
Jordan RNN

67. Output
Input
Previous State
State/ Hidden
Weights V
Weights U
Copy
(Delayed)
Weights W
Jordan RNN

68. Experimental setup
<rope> <rope> <air_cushion> <bumper> <bubble> <rocket> <candy> <Om_Nom>
Grid
XY
Candy distance
Om Nom distance
Input Representation

69. 0
400
200
300
100
Rope
Cuts(1)
Rope
Cuts(2)
Rocket
Presses
Air- Cushion
Presses
Bubble
Bursts
Void
Actions
Presented actions in the 100 playable levels test set altogether
Problem, Un-balanced Data

70. Balancing
COI
Data Representation
Training
levels #
Network
Type
Setup
Hidden
Neurons #
TestId
Balanced
N
All
100
Elman
10 networks
50traininglevels
0.0001 error
10 runs
37
Test 1
Balanced
Y
All
100
Elman
N
Y
XY
10, 50, 100
Elman
6 networks
50traininglevels
0.0001 error
6 runs
37
Test 2
4
Y
XY
10, 50, 100
Elman
N
Y
XY
150
Jordan
8 networks
50traininglevels
0.01 error
8 runs
5, 10, 15, 20,25, 30, 35, 40
Test 3
4
Y
XY
50
Jordan
4
Y
XY
50
Elman
N
Y
XY
10, 50, 100
Jordan
8 networks
50traininglevels
0.02 error
8 runs
5, 10, 15, 20,25, 30, 35, 40
Test 4
2
Y
XY
10, 50, 100
Jordan
N
N
Cookie distance
10, 50, 100
Elman
8 networks
50traininglevels
0.02 error
5 runs
5
Test 5
N
N
XY
10, 50, 100
Elman

72. Presentation Timeline
•The problem, the solution and what others are doing
•The game and the research
•Ropossum: the framework
–Content generation
–Playability: Other Models?
–Authoring tool
•Results and analysis
•Future Perspectives

73. Ropossum: RealtimeAuthoring Tool

74. Ropossum: RealtimeAuthoring Tool

75. Ropossum: RealtimeAuthoring Tool

76. GE Context Free Grammars
<level>::=<candy><Om_Nom><components> <candy>::=candy(<x>,<y>) <Om_Nom>::=Om_Nom(<x>,<y>) <components>::=<rope><air_cush><bumper><rocket><bubble><more_components> <more_components>::=<component>|<component><more_components> <component>::=<rope>|<air_cush>|<rocket>|<bumper>|<bubble> <rope>::=rope(<x>,<y>,<rope_length>) <rocket>::=rocket(<x>,<y>,<rocket_dir>) <air_cush>::=air_cush(<x>,<y>,<air_cush_dir>) <bumper>::=bumper(<x>,<y>,<bumper_dir>) <bubble>::=bubble(<x>,<y>) <x>::=[0, 260] <y>::=[0, 420] <rope_length>::=[0, 170] <air_cush_dir>::= 0 | 1<bumper_dir>::=[0, 7] <rocket_dir>::=[0, 7]

77. Ropossum Demo

78. “Ropossum: An Authoring Tool for Designing, optimizing and Solving Cut the Rope Levels”
AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2013

79. Presentation Timeline
•The problem, the solution and what others are doing
•The game and the research
•Ropossum: the framework
–Content generation
–Playability: Other Models?
–Authoring tool
•Results and analysis
•Future Perspectives

80. Selected InterestingResults
•35% of the playable levels haven’t been branch cut [direct solution]
•72.3% is the percentage of void action in the generated levels [relatively short levels?]
•29.8 ∓58.3 sec for checking a playable level.
•210% ∓167.6 for checking a non-playable level
–115 and 638 nodes is the average number of explored nodes for generated playable and non-playable levels [more explored nodes]
•10 min is the average time for generating a playable level

81. Average and standard deviation values of the four components
extracted from 500 generated levels
Frequency Analysis

82. Axialityof 0.1X, 0.9Y
Axialityof 1X, 0.4Y
AxialityMeasure

83. Axialityhistogram over 500 generated levels
AxialityMeasure

84. Density of 0.036
Density of 1
Density Measure 휎= 푖=1 푛(푥푖− 푥) 푛

85. The histogram of the density measure for the 500 generated levels
Density
Number of levels
Density Measure

86. Density-Axialityhistogram over 500 generated levels
Axiality-Density Histogram

87. Usedvs. Presentedcomponents by the playability agent over 100 generated playable levels
0
2
1
1.5
0.5
Ropes
Rockets
Air- Cushions
Bubbles
Bumpers
# activatedcomponents

88. Presentation Timeline
•The problem, the solution and what others are doing
•Ropossum; the game and the research
•Ropossum; the framework
–Content generation
–Playability
–Authoring tool
•Results and analysis
•Future Perspectives

89. Marry someoneand break your addiction
Employedor NOT
Employed people are 2Xmore likely to feel
anxiety and 2.25Xlikely to experience anger
Singlevs. Married
You are X2likely to be addicted on angry birds if
you are single than if you are married

90. 6%
Very
anxious
17%
Somewhat anxious
22%
Neither
32%
Somewhat relaxed
23%
Very
relaxed
Player Emotion

91. Simple
Easy
Just right
Tough
3%
6%
60%
31%
Difficulty

92. Live Demowww.mohammadshaker.com/ropossum.html

93. Fasten your seatbeltA new GAME era is emerging...

94. Thank you