Adaptive GamesContent Generation     “Mario”          Mohammad Shaker     Department of Artificial Intelligence         IT...
Outline•   Readings•   Motivation•   The proposed approach•   Experiments•   ANN Implementation•   Results•   Conclusion
Readings• Towards Automatic Personalized Content  Generation for Platform Games  Noor Shaker, Georgios N. Yannakakis, Memb...
Motivation
Motivation
Motivation
Motivation
Motivation
Motivation
Motivation
The Big Picture
The Big PictureGame       Player
The Big PictureGame       Player
The Big PictureGame                       Player       Player Experience            Model
The Big PictureGame                       Player       Player Experience            Model       Game Adaptation
The Big PictureGame                       Player       Player Experience            Model       Game Adaptation
The Big PictureGame                       Player       Player Experience            Model       Game Adaptation
The Big PictureGame                       Player       Player Experience            Model       Game Adaptation
The Game
The Game
Open Questions!   Session period? (frequency of adaptation)   The most useful information about game content?   Game as...
Open Questions!   Session period? (frequency of adaptation)   The most useful information about game content?   Game as...
ApproachDesign
ApproachDesign     Collect            Data
Approach                      ModelDesign     Collect   Player’s            Data     Emotion
Data Collection 40 small levels(one-third of usual size) 600 game pairs Features       Six controllable features     ...
Data Collection 40 small levels(one-third of usual size) 600 game pairs Features       Six controllable features     ...
Data Collection - Controllable Features   number of gaps   average width of gaps   number of enemies   number of power...
Experiments
Experiment 1   How long the game session should be in order to be    able to extract useful information?
Experiment 1   How long the game session should be in order to be    able to extract useful information?
Segmentation
Segmentation
Content-Driven Preference Learning• It’s the use of genetic algorithms to evolve the  weight of neural networks to learn p...
Content-Driven Preference Learning• It’s the use of genetic algorithms to evolve the  weight of neural networks to learn p...
Content-Driven Preference Learning• It’s the use of genetic algorithms to evolve the  weight of neural networks to learn p...
Content-Driven Preference Learning• It’s the use of genetic algorithms to evolve the  weight of neural networks to learn p...
Content-Driven Preference Learning• It’s the use of genetic algorithms to evolve the  weight of neural networks to learn p...
Content-Driven Preference Learning• It’s the use of genetic algorithms to evolve the  weight of neural networks to learn p...
Content-Driven Preference Learning                Feature               extraction            NeuroEvolutionary           ...
Feature            Feature             Feature    extraction         extraction          extractionNeuroEvolutionary   Neu...
Experiment 2   How can we extract the most useful information    about game content?
Experiment 2   How can we extract the most useful information    about game content?
Game Content Representation   Statistical features   Sequences
Game Content Representation   Statistical features       Six controllable features       Used for level generation   S...
Game Content Representation   Statistical features        Six controllable features        Used for level generation  ...
Sequence Mining
Sequence Mining
Sequence Mining-SPADE              SPADE              occurrences                      Frequent  40 levels           Subse...
Content-Driven Preference Learning                     ANN- Statistical   NeuroEvolutionary    Player’s  features        P...
Experiment 3   What are the game aspects that have the major    affect on player experience?
Experiment 3   What are the game aspects that have the major    affect on player experience?
Content-Driven Preference LearningStatistical                    ANN- features               Feature    NeuroEvolutionary ...
ANN Implementation
ANN Implementation• Multilayer perceptrons (MLPs)   o ANN inputs      • Controllable features      • Sequences as features...
ANN Training• Genetic algorithms (GAs)  o No prescribed target outputs
ANN Training• Genetic algorithms (GAs)  o No prescribed target outputs• How it works?
ANN Training• Genetic algorithms (GAs)  o No prescribed target outputs• How it works?                players’            m...
ANN Training• Genetic algorithms (GAs)  o No prescribed target outputs• How it works?                players’             ...
ANN Implementation
ANN Implementation SF CF
ANN Implementation SF CF
Optimizing Neural Networks Topologies•   2 hidden layers (Max.)
Optimizing Neural Networks Topologies•   2 hidden layers (Max.)•   Multiple experiments       1 hidden layer, Adding two ...
Optimizing Neural Networks Topologies•   2 hidden layers (Max.)•   Multiple experiments       1 hidden layer, Adding two ...
Optimizing Neural Networks Topologies•   2 hidden layers (Max.)•   Multiple experiments       1 hidden layer, Adding two ...
ANN Adaptation
ANN Implementation SF CF
ANN AdaptationSF        Prediction of            player’sCF          emotion
ANN AdaptationSF               Prediction of                   player’sCF                 emotion     Gaps #: 4-10     Gap...
ANN AdaptationSF                       Prediction of                           player’sCF                         emotionE...
ANN AdaptationSF                       Prediction of                           player’sCF                         emotionE...
ANN Adaptationlevel1      level2           level20       level21       level50    Adapt            Adapt         Adapt    ...
Neural Networks Input RepresentationStatistical                    ANN- features               Feature    NeuroEvolutionar...
Game Content RepresentationStatistical features               FeatureSequential    selection features
Game Content Representation
Game Content Representation      The best-performing MLP models evaluated on occurrencesof frequent subsequences of length...
MLPs Performance on Full Information           about Game Content The topology and performance of the best MLP models eval...
Results   The performance and topologies of MLP models evaluated on full and partialinformation of game content using stat...
Content-Driven Preference LearningStatistical                    ANN- features               Feature    NeuroEvolutionary ...
Conclusion   Combining both sequential and statistical features    gives better results in predicting players reported   ...
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Adaptive Games Content Generation - 2D Mario

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Adaptive Games Content Generation - 2D Mario

  1. 1. Adaptive GamesContent Generation “Mario” Mohammad Shaker Department of Artificial Intelligence IT University of Damascus Seminar of Artificial Neural Networks ZGTR
  2. 2. Outline• Readings• Motivation• The proposed approach• Experiments• ANN Implementation• Results• Conclusion
  3. 3. Readings• Towards Automatic Personalized Content Generation for Platform Games Noor Shaker, Georgios N. Yannakakis, Member, IEEE, and Julian Togelius, Member, IEEE• Feature Analysis for Modeling Game Content Quality Noor Shaker, Georgios N. Yannakakis, Member, IEEE, and Julian Togelius, Member, IEEE
  4. 4. Motivation
  5. 5. Motivation
  6. 6. Motivation
  7. 7. Motivation
  8. 8. Motivation
  9. 9. Motivation
  10. 10. Motivation
  11. 11. The Big Picture
  12. 12. The Big PictureGame Player
  13. 13. The Big PictureGame Player
  14. 14. The Big PictureGame Player Player Experience Model
  15. 15. The Big PictureGame Player Player Experience Model Game Adaptation
  16. 16. The Big PictureGame Player Player Experience Model Game Adaptation
  17. 17. The Big PictureGame Player Player Experience Model Game Adaptation
  18. 18. The Big PictureGame Player Player Experience Model Game Adaptation
  19. 19. The Game
  20. 20. The Game
  21. 21. Open Questions! Session period? (frequency of adaptation) The most useful information about game content? Game aspects with major affect on player experience?
  22. 22. Open Questions! Session period? (frequency of adaptation) The most useful information about game content? Game aspects with major affect on player experience?
  23. 23. ApproachDesign
  24. 24. ApproachDesign Collect Data
  25. 25. Approach ModelDesign Collect Player’s Data Emotion
  26. 26. Data Collection 40 small levels(one-third of usual size) 600 game pairs Features  Six controllable features  Players preferences of engagement
  27. 27. Data Collection 40 small levels(one-third of usual size) 600 game pairs Features  Six controllable features  Players preferences of engagement
  28. 28. Data Collection - Controllable Features number of gaps average width of gaps number of enemies number of powerups number of boxes Enemies placement  Around horizontal boxes  Around gaps  Random placement
  29. 29. Experiments
  30. 30. Experiment 1 How long the game session should be in order to be able to extract useful information?
  31. 31. Experiment 1 How long the game session should be in order to be able to extract useful information?
  32. 32. Segmentation
  33. 33. Segmentation
  34. 34. Content-Driven Preference Learning• It’s the use of genetic algorithms to evolve the weight of neural networks to learn preference data.
  35. 35. Content-Driven Preference Learning• It’s the use of genetic algorithms to evolve the weight of neural networks to learn preference data.Levels
  36. 36. Content-Driven Preference Learning• It’s the use of genetic algorithms to evolve the weight of neural networks to learn preference data.Levels Segmentation
  37. 37. Content-Driven Preference Learning• It’s the use of genetic algorithms to evolve the weight of neural networks to learn preference data. FeatureLevels Segmentation extraction
  38. 38. Content-Driven Preference Learning• It’s the use of genetic algorithms to evolve the weight of neural networks to learn preference data. NeuroEvolutionary Feature preferenceLevels Segmentation extraction learning
  39. 39. Content-Driven Preference Learning• It’s the use of genetic algorithms to evolve the weight of neural networks to learn preference data. NeuroEvolutionary Feature Player’sLevels Segmentation preference extraction Engagement learning
  40. 40. Content-Driven Preference Learning Feature extraction NeuroEvolutionary preference learning
  41. 41. Feature Feature Feature extraction extraction extractionNeuroEvolutionary NeuroEvolutionary NeuroEvolutionary preference preference preference learning learning learning
  42. 42. Experiment 2 How can we extract the most useful information about game content?
  43. 43. Experiment 2 How can we extract the most useful information about game content?
  44. 44. Game Content Representation Statistical features Sequences
  45. 45. Game Content Representation Statistical features  Six controllable features  Used for level generation Sequences
  46. 46. Game Content Representation Statistical features  Six controllable features  Used for level generation Sequences  Numbers representing different types of game content o Platform structure, S o Enemies placement, Ep o Enemies and items placement, D
  47. 47. Sequence Mining
  48. 48. Sequence Mining
  49. 49. Sequence Mining-SPADE SPADE occurrences Frequent 40 levels Subseq. seq.
  50. 50. Content-Driven Preference Learning ANN- Statistical NeuroEvolutionary Player’s features Preference Engagement Learning ANN-Sequential NeuroEvolutionary Player’s features Preference Engagement Learning
  51. 51. Experiment 3 What are the game aspects that have the major affect on player experience?
  52. 52. Experiment 3 What are the game aspects that have the major affect on player experience?
  53. 53. Content-Driven Preference LearningStatistical ANN- features Feature NeuroEvolutionary Player’sSequential selection Preference Engagement features Learning
  54. 54. ANN Implementation
  55. 55. ANN Implementation• Multilayer perceptrons (MLPs) o ANN inputs • Controllable features • Sequences as features o ANN output • Value of the engagement preference
  56. 56. ANN Training• Genetic algorithms (GAs) o No prescribed target outputs
  57. 57. ANN Training• Genetic algorithms (GAs) o No prescribed target outputs• How it works?
  58. 58. ANN Training• Genetic algorithms (GAs) o No prescribed target outputs• How it works? players’ magnitude of reported corresponding emotional model (ANN) preferences output
  59. 59. ANN Training• Genetic algorithms (GAs) o No prescribed target outputs• How it works? players’ reported emotional preferences - magnitude of corresponding model (ANN) output
  60. 60. ANN Implementation
  61. 61. ANN Implementation SF CF
  62. 62. ANN Implementation SF CF
  63. 63. Optimizing Neural Networks Topologies• 2 hidden layers (Max.)
  64. 64. Optimizing Neural Networks Topologies• 2 hidden layers (Max.)• Multiple experiments  1 hidden layer, Adding two neurons at each step  2 neurons - 8 neurons
  65. 65. Optimizing Neural Networks Topologies• 2 hidden layers (Max.)• Multiple experiments  1 hidden layer, Adding two neurons at each step  2 neurons - 8 neurons  2 hidden layers, Adding two neurons at each step  1st Hidden layer  2 neurons - 10 neurons  2nd Hidden layer  2 neurons - 8 neurons
  66. 66. Optimizing Neural Networks Topologies• 2 hidden layers (Max.)• Multiple experiments  1 hidden layer, Adding two neurons at each step  2 neurons - 8 neurons  2 hidden layers, Adding two neurons at each step  1st Hidden layer  2 neurons - 10 neurons  2nd Hidden layer  2 neurons - 8 neurons
  67. 67. ANN Adaptation
  68. 68. ANN Implementation SF CF
  69. 69. ANN AdaptationSF Prediction of player’sCF emotion
  70. 70. ANN AdaptationSF Prediction of player’sCF emotion Gaps #: 4-10 Gaps width: 10-30 Gaps placement: 0-1 Switch:0-1
  71. 71. ANN AdaptationSF Prediction of player’sCF emotionExhaustive search Gaps #: 4-10 Gaps width: 10-30 Gaps placement: 0-1 Switch:0-1
  72. 72. ANN AdaptationSF Prediction of player’sCF emotionExhaustive search Gaps #: 4-10 Gaps width: 10-30 Gaps placement: 0-1 Switch:0-1
  73. 73. ANN Adaptationlevel1 level2 level20 level21 level50 Adapt Adapt Adapt Adapt
  74. 74. Neural Networks Input RepresentationStatistical ANN- features Feature NeuroEvolutionary Player’sSequential selection Preference Engagement features Learning
  75. 75. Game Content RepresentationStatistical features FeatureSequential selection features
  76. 76. Game Content Representation
  77. 77. Game Content Representation The best-performing MLP models evaluated on occurrencesof frequent subsequences of length three extracted from the 40 levels
  78. 78. MLPs Performance on Full Information about Game Content The topology and performance of the best MLP models evaluated on full andpartial information about game content. the MLP performance presented is the average performance over 20 runs.
  79. 79. Results The performance and topologies of MLP models evaluated on full and partialinformation of game content using statistics from the game window and from two and three segments to which the window has been divided. The performance presented is the average over five runs.
  80. 80. Content-Driven Preference LearningStatistical ANN- features Feature NeuroEvolutionary Player’sSequential selection Preference Engagement features Learning
  81. 81. Conclusion Combining both sequential and statistical features gives better results in predicting players reported emotional state. Partitioning the level causes a significant decrease (p < 0.05) in the accuracy of predicting player’s reported engagement. This suggests that there might be information loss because of decomposing the data and that this loss causes a performance decrease. Multiple perspectives can be done in reference to this study which is already going on!
  82. 82. Thank you!

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