Prof. Pier Luca Lanzi discusses using data-driven game design and machine learning techniques like player modeling and gameplay analysis tools to balance multiplayer first-person shooters. He proposes using the distribution of kills and scores among players as a proxy to evaluate balancing. His research also looks at using AI to automatically design game maps and levels to improve balancing, as well as generative adversarial networks to generate new Doom levels.

1. Prof. Pier Luca Lanzi
Data-Driven Game Design
Pier Luca Lanzi

4. design
developanalyze
data

5. Data Driven Game Design
machine learning and data science applied to game data
player modeling, gameplay analysis tools, level design,
etc.

6. Balancing the Gameplay
(with Daniele Loiacono and Riccardo Stucchi)

7. 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

8. Balancing multiplayer games is both
a design and a matchmaking problem

9. How the map affect the match balancing?
Can we automatically design
map to improve balancing?

11. BOT1 SKILL
BOT2SKILL
BOT1 SKILL
BOT2SKILL
EVOLUTION
>84% 66%-84% 50%-66% 33%-50% 16%-33%

12. f=0.93 f=0.98

13. Pacing the Gameplay
(Daniele Loiacono and Luca Arnaboldi)

19. AI Assisted Platform Design
(with Daniele Loiacono and Antonio Aramini)

20. http://ian-albert.com/games/super_mario_bros_maps/

21. 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

22. 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

23. Estimating Difficulty

24. Estimating Success Probability

25. Estimating Success Probability

26. Examples of minimum difficulty path (in red) displayed by the framework.

27. Pier Luca Lanzi – 20 Luglio 2018 – Campus Party

28. DOOM Level Generation using
Generative Adversarial Networks
(with Daniele Loiacono and Edoardo Giacomello)

29. DOOM (1993)

30. https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners

31. Wasserstein Generative Adversarial Networks

32. Pier Luca Lanzi – 20 Luglio 2018 – Campus Party
DOOM Levels 32
Area 61.01
Rooms 20
Enemies 21
Author Sanftenberg
… …

33. True or Generated?
Vettore Casuale
DOOM Levels
Generated Levels
D Network
G Network

34. True or Generated?
Random Vector
DOOM Levels
Generated Levels
D Network
G Network
Area Convex?
Main Axis # Rooms
… …

36. 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

37. 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

38. http://www.polimigamecollective.org
http://www.facebook.com/polimigamecollective
http://www.youtube.com/PierLucaLanzi
pierluca.lanzi@polimi.it