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Computational Intelligence, Analytics and Computer Games

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 . Zahid.halim@giki.edu.pk . Layout. Why Computer Games ?

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Computational Intelligence, Analytics and Computer Games

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  1. Computational Intelligence, Analytics and Computer Games Dr. Zahid Halim Faculty of Computer Science and Engineering GhulamIshaq Khan Institute of Engineering Sciences and Technology, Topi. Zahid.halim@giki.edu.pk

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

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

  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

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

  6. Predator/prey Games Search Space • 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. ∆xr Connection Edges Connection Edges Connection Edges ∆yr Nu ∆xg Nd ∆yg Nl ∆xb Nr ∆yb 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.

  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. Duration of game Appropriate level of challenge Diversity

  13. Usability Combined Fitness 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

  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

  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

  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)

  20. Tools • www.gameanalytics.com • www.playtomic.com • www.honeytracks.com • www.kontagent.com

  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

  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.

  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

  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.

  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

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

  27. Thank you for your patience Questions This presentation is uploaded at http://ming.org.pk/zahid.htm

  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:Maximizingthe value of player data. Springer.

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