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Computational Intelligence, Analytics and Computer Games Dr. Zahid Halim Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering.

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Presentation on theme: "Computational Intelligence, Analytics and Computer Games Dr. Zahid Halim Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering."— Presentation transcript:

1 Computational Intelligence, Analytics and Computer Games Dr. Zahid Halim Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi.

2 Layout Why Computer Games ? Computational Intelligence and Computer Games Game analytics basics Game data mining – Clustering – Classification – Association Rules Mining 2

3 Why Computer Games? 49% of U.S. households own a dedicated game console The average game player age is: 30 years Age 3

4 Why Computer Games? 42% of game players believe that computer and video games give them the most value for their money, compared with DVDs, music or going out to the movies Gamers who are playing more video games than they did three years ago are spending less time: – 59% playing board games – 50% going to the movies – 47% watching TV – 47% watching movies at home 62% of gamers play games with others, either in-person or online 78% of gamers who play with others do so at least one hour per week 4

5 Money Matters! Total: $24.75 Billion But its not every thing! 5

6 Search Space Predator/prey Games 14 X 14 grid excluding the boundary walls. Couple of walls at fixed positions and of size 7 cells There is one player controlled by the human player. There are N (0-20)other pieces of M (1,2 and 3) types Maximum duration 100 game steps Finish game – Agent dies – Maximum score is achieved – Maximum game steps utilized Movement logic – No movement – Clockwise – Counter clockwise – Random – Random direction Collision logic – no effect – random relocation to a new location on the grid – death. Scoring logic – +1, -1, 0

7 Chromosome Encoding for Genetic Algorithm

8 Entertainment Metrics Duration of the Game Appropriate Level of Challenge Diversity Usability

9 Rule Based Controller The controller looks up, down, left and right. It notes the nearest piece (if any) in each of the four directions, and then it simply moves one step towards the nearest score increasing piece If there are no score increasing piece present it determines its step according to the following priority list – Move in the direction which is completely empty – If more than one directions are empty move towards the farthest wall – Move in the direction which contains a score neutral piece – Move in the direction which contains a score decreasing piece – Move in the direction which contains a death causing piece

10 Neural Network Based Controller Multi-layer fully feed forward 6 neurons in the input layer 5 neurons in the hidden layer 4 output layer neurons Sigmoid activation function Edges weights -5 to +5. ∆ xr ∆ xg ∆ xb ∆ yg ∆ yb ∆ yr NuNu NdNd NlNl NrNr C o n n e c ti o n E d g e s

11 Experimentation Setup 10 chromosomes are randomly initialized by the GA One offspring is created for each chromosome – Duplicating it – Mutating any one of its gene Results in 20 chromosomes from which 10 best chosen 100 generations

12 Appropriate level of challenge Duration of game Diversity PredatorsMovement logicCollision logic (a) RGBRGBR-RR-GR-GR-BR-BR-AR-AG-RG-RG-GG-BG-BG-AG-AB-R 31713303211001021 Collision logicScoring logic B -GB-BB-AB-AA-RA-RA-GA-GA-BR-RG-GB-BA-RA-RA-GA-GA-BA-BG-RG-RB-RB-RB-GB-G 222002 0 00 0 PredatorsMovement logicCollision logic (b) RGBRGBR-RR-GR-GR-BR-BR-AR-AG-RG-RG-GG-BG-BG-AG-AB-R 01810013221120011 Collision logicScoring logic B -GB-BB-AB-AA-RA-RA-GA-GA-BR-RG-GB-BA-RA-RA-GA-GA-BA-BG-RG-RB-RB-RB-GB-G 1111020101 1 PredatorsMovement logicCollision logic (a) RGBRGBR-RR-GR-GR-BR-BR-AR-AG-RG-RG-GG-BG-BG-AG-AB-R 10011003121100202 Collision logicScoring logic B -GB-BB-AB-AA-RA-RA-GA-GA-BR-RG-GB-BA-RA-RA-GA-GA-BA-BG-RG-RB-RB-RB-GB-G 2222021101 10 PredatorsMovement logicCollision logic (b) RGBRGBR-RR-GR-GR-BR-BR-AR-AG-RG-RG-GG-BG-BG-AG-AB-R 778243202200212 Collision logicScoring logic B -GB-BB-AB-AA-RA-RA-GA-GA-BR-RG-GB-BA-RA-RA-GA-GA-BA-BG-RG-RB-RB-RB-GB-G 00020200011110 PredatorsMovement logicCollision logic (a) RGBRGBR-RR-GR-GR-BR-BR-AR-AG-RG-RG-GG-BG-BG-AG-AB-R 0100242012120201 Collision logicScoring logic B -GB-BB-AB-AA-RA-RA-GA-GA-BR-RG-GB-BA-RA-RA-GA-GA-BA-BG-RG-RB-RB-RB-GB-G 1021020 10 0 PredatorsMovement logicCollision logic (b) RGBRGBR-RR-GR-GR-BR-BR-AR-AG-RG-RG-GG-BG-BG-AG-AB-R 0170444120001221 Collision logicScoring logic B -GB-BB-AB-AA-RA-RA-GA-GA-BR-RG-GB-BA-RA-RA-GA-GA-BA-BG-RG-RB-RB-RB-GB-G 112102 00 10

13 Usability Combined Fitness PredatorsMovement logicCollision logic (a) RGBRGBR-RR-GR-GR-BR-BR-AR-AG-RG-RG-GG-BG-BG-AG-AB-R 201819122200221202 Collision logicScoring logic B -GB-BB-AB-AA-RA-RA-GA-GA-BR-RG-GB-BA-RA-RA-GA-GA-BA-BG-RG-RB-RB-RB-GB-G 22020210100 00 PredatorsMovement logicCollision logic (b) RGBRGBR-RR-GR-GR-BR-BR-AR-AG-RG-RG-GG-BG-BG-AG-AB-R 1920 442202222022 Collision logicScoring logic B -GB-BB-AB-AA-RA-RA-GA-GA-BR-RG-GB-BA-RA-RA-GA-GA-BA-BG-RG-RB-RB-RB-GB-G 202202 1 110 PredatorsMovement logicCollision logic (a) RGBRGBR-RR-GR-GR-BR-BR-AR-AG-RG-RG-GG-BG-BG-AG-AB-R 1583110120010120 Collision logicScoring logic B -GB-BB-AB-AA-RA-RA-GA-GA-BR-RG-GB-BA-RA-RA-GA-GA-BA-BG-RG-RB-RB-RB-GB-G 22000011 1 PredatorsMovement logicCollision logic (b) RGBRGBR-RR-GR-GR-BR-BR-AR-AG-RG-RG-GG-BG-BG-AG-AB-R 11203242220222011 Collision logicScoring logic B -GB-BB-AB-AA-RA-RA-GA-GA-BR-RG-GB-BA-RA-RA-GA-GA-BA-BG-RG-RB-RB-RB-GB-G 11200201110 01 1 3,34 54 56 66 …. !!!

14 Controller Learning Ability

15 User Survey 10 subjects Conducted in two different sets on different days – Rule based controller – ANN based controller – Each individual was given 6 games – Play 2 times

16 Research in Computer Games Game User Research (GUR) Game analytics is becoming and increasingly important area of BI for industry Key terms – Game analytics – Metrics – Telemetry – Used interchangeably Games released in patches – Based on telemetry release subsequent patches 16

17 Game data mining Modern digital games – Simple applications – Incredibly sophisticated information systems – Common for all is that need to keep track of the actions of players and calculate a response Telemetry data 17

18 Few examples of game data mining Find weak spots in a games’ design Figure out how to convert non-paying to paying users Discover geographical patterns in our player community Figure out how players spend their time when playing How much time they spend playing Predict when they will stop playing Which assets that are not getting used Develop better AI-controlled opponents Explore and use of social grouping 18

19 Data Formats Game telemetry is importantly concerned about how data are stored and accessed. SQL has problems with scaling up – SSD enhancements – More “elastic” means of data storage on cloud computing – These new database formats are commonly referred to as “NoSQL” (and NewSQL) and have become popular in big data contexts due to the need for fast, efficient data access. MongoDB Cassandra Couch HBase (Hadoop) 19

20 Tools 20

21 Clustering Players – Battlefield 1/3 First person shooter with tactical wargame elements, – Online multiplayer – Up to 32 players – Including off-line capability. BF2BC2 (battlefield 2 bad company) Each player controls one character in a team, playing against another team. Modes of play = “kits” – Assault, Demolition, Specialist, Recon and Support Drachen et al. ( 2012 ) used behavior telemetry data from randomly selected 10,000 BF2BC2 players, all playing on PC. A total of 11 variables (features) were included in their analysis – Score – Skill level – Total playtime – Kill/Death ratio – Accuracy – Score per minute – Deaths per minute/Kills per minute – Rounds played – Kit stats – Vehicle use 21

22 Clustering Players – Battlefield 2/3 Pre-processing and normalization of the telemetry data Applied Clustering K-means Simplex Volume Maximization (SIVM) – Does not look for commonalities between players, but rather extreme profiles that do not reside in dense cluster regions – Both algorithms resulted in seven clusters. 22

23 Clustering Players – Battlefield 3/3 Assassins – Extremely high Kill/Death ratios but surprisingly low-middle playtime. Veterans – Are the all-round elite. – Represent a small fraction of the players, 2–4%. Target dummies – Opposite of the Veterans. Assault-Recon – High performance with some of the kits – They also exhibit low accuracy – About 1.5% of the players are included in this cluster Medic-Engineer – Very high skill levels and accuracy, score, drive in vehicles a lot. Assault – They die a lot – Have invested a lot of playtime into the game, with low skill, K/D ratio and accuracy. Driver Engineers – Have extremely high vehicle times - driving, sailing or flying the various kinds – They have high playtimes, scores and accuracy, very high K/D ratio but kill very few players, and also die rarely 23

24 Classification --Tomb Raider: Underworld 1/2 Self-Organizing Map – A form of ANN looks for low- dimensional representations of the input data Used gameplay metrics data from 1,365 players of Tomb Raider: Underworld SOM used to classify players into distinct groups based on their behavior. The analysis revealed four distinct classes of behavior. 24

25 Classification --Tomb Raider: Underworld 2/2 Class 1 (Veterans)-(8.68%) – Very few death events – Fast completion times. Generally perform very well in the game Class 2 (Solvers)-(22.12%) – Die rarely – Take a long time to complete the game Class 3 (Pacifists)-(46.18%) – Dying primarily from enemies – Completion time relatively fast and help requests minimal Class 4 (Runners)-(16.56%) – Die often and by enemies & environment – Often use of help system but complete the game very fast 25

26 Frequent Pattern Mining Frequent Sequence Mining By virtue of being discrete-time systems, computer games constantly generate large amounts of sequential data. 26

27 Questions This presentation is uploaded at Thank you for your patience

28 Bibliography Halim, Z., R. Baig, and K. Zafar. "Evolutionary Search in the Space of Rules for Creation of New Two-Player Board Games." International Journal on Artificial Intelligence Tools (2013). Entertainment Software Association. "Essential facts about the computer and video game industry." (2012). El-Nasr, M. S., Drachen, A., & Canossa, A. (2013). Game analytics:Maximizing the value of player data. Springer. 28

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