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Data Driven Game Design @ Campus Party 2018

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Presentation given at Campus Party 2018 in Milan.

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Data Driven Game Design @ Campus Party 2018

  1. 1. Prof. Pier Luca Lanzi Data-Driven Game Design Pier Luca Lanzi
  2. 2. design developanalyze data
  3. 3. Data Driven Game Design machine learning and data science applied to game data player modeling, gameplay analysis tools, level design, etc.
  4. 4. Balancing the Gameplay (with Daniele Loiacono and Riccardo Stucchi)
  5. 5. Pier Luca Lanzi – 20 Luglio 2018 – Campus Party Balancing Multiplayer First-Person Shooters • Providing the “right amount” of challenge is very important. Multiplayer games are more difficult to balance • Balance depends on the players’ skill, the playing strategies, the game environment, the weapons, etc. • How can we evaluate if an FPS is balanced? It is mainly subjective! However, the distribution of kills/scores among players could be a good proxy • For example, in a 2-players match best player should kill the opponent less than twice the time it has been killed 7
  6. 6. Balancing multiplayer games is both a design and a matchmaking problem
  7. 7. How the map affect the match balancing? Can we automatically design map to improve balancing?
  8. 8. BOT1 SKILL BOT2SKILL BOT1 SKILL BOT2SKILL EVOLUTION >84% 66%-84% 50%-66% 33%-50% 16%-33%
  9. 9. f=0.93 f=0.98
  10. 10. Pacing the Gameplay (Daniele Loiacono and Luca Arnaboldi)
  11. 11. AI Assisted Platform Design (with Daniele Loiacono and Antonio Aramini)
  12. 12. http://ian-albert.com/games/super_mario_bros_maps/
  13. 13. Pier Luca Lanzi – 20 Luglio 2018 – Campus Party AI Framework to Assist Level Designers • Conceptual model Graph representation of levels Probabilistic estimation of difficulty • Definition of a set of metrics to evaluate levels in terms of difficulty and probability of completion • Model validation Single jumps human playtesting Double jumps human playtesting • Implementation in Unity editor 21
  14. 14. Pier Luca Lanzi – 20 Luglio 2018 – Campus Party Components of the conceptual model • Jumps  Trivial, Simple, Falling, Reentrant • Platforms  Static  Moving  Fading  Spiked • Game elements  Enemies  Collectible items 22
  15. 15. Estimating Difficulty
  16. 16. Estimating Success Probability
  17. 17. Estimating Success Probability
  18. 18. Examples of minimum difficulty path (in red) displayed by the framework.
  19. 19. Pier Luca Lanzi – 20 Luglio 2018 – Campus Party
  20. 20. DOOM Level Generation using Generative Adversarial Networks (with Daniele Loiacono and Edoardo Giacomello)
  21. 21. DOOM (1993)
  22. 22. https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners
  23. 23. Wasserstein Generative Adversarial Networks
  24. 24. Pier Luca Lanzi – 20 Luglio 2018 – Campus Party DOOM Levels 32 Area 61.01 Rooms 20 Enemies 21 Author Sanftenberg … …
  25. 25. True or Generated? Vettore Casuale DOOM Levels Generated Levels D Network G Network
  26. 26. True or Generated? Random Vector DOOM Levels Generated Levels D Network G Network Area Convex? Main Axis # Rooms … …
  27. 27. Pier Luca Lanzi – 20 Luglio 2018 – Campus Party Data-Driven Game Design • Video games can generate and collect huge amount of data • These data contain potentially useful information that can help improving the design or inspiring new designs • Advanced data mining methods are required to analyze such data so as to produce models and knowledge to support designers 36
  28. 28. Pier Luca Lanzi – 20 Luglio 2018 – Campus Party References • Luigi Cardamone, Pier Luca Lanzi, Daniele Loiacono:TrackGen: An interactive track generator for TORCS and Speed-Dreams. Appl. Soft Comput. 28: 550-558 (2015)Player Modeling • Luigi Cardamone, Pier Luca Lanzi, Daniele Loiacono, Enrique Onieva:Advanced overtaking behaviors for blocking opponents in racing games using a fuzzy architecture. Expert Syst. Appl. 40(16): 6447-6458 (2013) • Daniele Loiacono, Luigi Cardamone, Pier Luca Lanzi:Automatic Track Generation for High-End Racing Games Using Evolutionary Computation. IEEE Trans. Comput. Intellig. and AI in Games 3(3): 245-259 (2011) • Luigi Cardamone, Daniele Loiacono, Pier Luca Lanzi:Learning to Drive in the Open Racing Car Simulator Using Online Neuroevolution. IEEE Trans. Comput. Intellig. and AI in Games 2(3): 176-190 (2010) • Daniele Gravina, Daniele Loiacono:Procedural weapons generation for unreal tournament III. GEM 2015: 1- 8 • Luca Galli, Pier Luca Lanzi, Daniele Loiacono:Applying data mining to extract design patterns from Unreal Tournament levels. CIG 2014: 1-8 • Pier Luca Lanzi, Daniele Loiacono, Riccardo Stucchi:Evolving maps for match balancing in first person shooters. CIG 2014: 1-8 • Pier Luca Lanzi, Daniele Loiacono, Emanuele Parini, Federico Sannicoló, Davide Jones, Claudio Scamporlino:Tuning mobile game design using data mining. IGIC 2013: 122-129 • Daniele Loiacono:Learning, evolution and adaptation in racing games. Conf. Computing Frontiers 2012: 277-284 • Matteo Botta, Vincenzo Gautieri, Daniele Loiacono, Pier Luca Lanzi:Evolving the optimal racing line in a high-end racing game. CIG 2012: 108-115 37
  29. 29. http://www.polimigamecollective.org http://www.facebook.com/polimigamecollective http://www.youtube.com/PierLucaLanzi pierluca.lanzi@polimi.it

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