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Why AI is shaping our games

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AI is used to create parts of our games. It provides intelligent enemy behavior, techniques such as pathfinding or can be used to generate in-game content procedurally. AI can also play our games. The idea to train computers to beat humans in game-like environments such as Jeopardy!, Chess, or soccer is not a new one. But can AI also design our games? The role of Artificial Intelligence in the game development process is constantly expanding. In this talk, Dr. Pirker will talk about the importance of AI in the past, the present, and especially the future of game development.

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Why AI is shaping our games

  1. 1. S C I E N C E * PA S S I O N * T E C H N O L O G Y WHY AI IS SHAPING OUR GAMES D R . J O H A N N A P I R K E R , T U G R A Z , A U S T R I A K L A G E N F U RT 2 0 1 9
  2. 2. AI MIRACLES..
  3. 3. “MAKING COMPUTERS ACT LIKE THEY DO IN THE MOVIES.”
  4. 4. 1. THE CAPABILITY OF A MACHINE TO IMITATE INTELLIGENT HUMAN BEHAVIOR.
 2. A BRANCH OF COMPUTER SCIENCE DEALING WITH THE SIMULATION OF INTELLIGENT BEHAVIOR IN COMPUTERS. Merriam-Webster defines artificial intelligence this way.
  5. 5. “REAL” AI ▸ 1. learn over time in response to changes in its environments ▸ (e.g. Netflix recommendations but not Twitter black lists) ▸ 2. what it learns should be interesting enough that it would take humans some effort to learn ▸ (Turing test)
  6. 6. AI IN GAMES ▸ … generate responsive, adaptive, & intelligent behaviour ▸ uses path finding, decision trees, data mining, PCG, … ▸ usually do not facilitate computer learning ▸ -> predetermined & limited set of responses to a limited set of inputs ▸ ILLUSION OF INTELLIGENCE ▸ good gameplay without environment restrictions ▸ learn & use from “real AI” strategies ▸ Learning Tamagotchi
  7. 7. ▸ decision trees (scripting) ▸ -> AI stupidity, predictive behaviour, loss of immersion ▸ pathfinding ▸ (Half Life, “Crouch Cover”) ▸ NPC behaviour in Doom ▸ NPCs fighting NPCs AI IN GAMES - ISSUES
  8. 8. PLAY GAMES. CONTRIBUTE CONTENT. DESIGN
 GAMES. UNDERSTAND 
 PLAYERS.
  9. 9. I. PLAY GAMES.
  10. 10. AI TO PLAY GAMES ROBOCUP
  11. 11. AI TO PLAY GAMES CHESS - IBM DEEP BLUE VS. GARRY KASPAROV (1997)  "I could feel — I could smell — a new kind of intelligence across the table,"
  12. 12. AI TO PLAY GAMES JEOPARDY! - IBM WATSON VS. KEN JENNINGS (2011)  "I could feel — I could smell — a new kind of intelligence across the table,"
  13. 13. AI TO PLAY GAMES GO - GOOGLE ALPHAGO (DEEPMIND) VS. LEE SEDOL (2016)
  14. 14. AI TO PLAY GAMES DEEPMIND VS. STARCRAFT II (2019)
  15. 15. AI TO PLAY GAMES http://gameaibook.org/book.pdf
  16. 16. ▸ Chess Two-player adversarial, deterministic, fully observable, branching factor ~35, ~70 turns ▸ Go Two-player adversarial, deterministic, fully observable, branching factor ~350, ~150 turns ▸ Frogger (Atari 2600) 1 player, deterministic, fully observable, bf 6, hundreds of ticks ▸ Halo 1.5 player, deterministic, partially observable, bf ???, tens of thousands of ticks ▸ Starcraft 2-4 players, stochastic, partially observable, bf > a million, tens of thousands of ticks ▸ Togelius AI TO PLAY GAMES
  17. 17. AI TO PLAY GAMES TRAIN AI HOW TO PLAY SNAKE (DEEP REINFORCEMENT LEARNING) On the left, the agent was not trained and had no clues on what to do whatsoever. The game on the right refers to the game after 100 iterations (about 5 minutes). The highest score was 83 points, after 200 iterations. https://github.com/maurock/snake-ga
  18. 18. AI TO PLAY GAMES TRAIN AI HOW TO PLAY STARCRAFT ‣ A Machine Learning API developed by Blizzard that gives researchers and developers hooks into the game. ‣ A dataset of half a million anonymised game replays,.   ‣ An open source version of DeepMind’s toolset, PySC2 ‣ A series of simple RL mini-games to test the performance of agents on specific tasks. https://deepmind.com/blog/deepmind-and-blizzard-open-starcraft-ii-ai-research-environment/
  19. 19. AI TO PLAY GAMES WHY USE AI TO PLAY GAMES? ▸ Playing to win vs playing for experience ▸ For experience: human-like, fun, predictable…? ▸ Playing in the player role vs playing in a non-player role http://gameaibook.org/book.pdf
  20. 20. METHODS ▸ Planning-Based ▸ Uninformed search (e.g. BFS),Informed search (e.g. A*), Evolutionary algorithms ▸ Reinforcement learning (training time) ▸ TD-learning / approximate dynamic programming, Evolutionary algorithms ▸ Supervised learning (requires play traces to learn from) ▸ Neural nets, k-nearest neighbors etc ▸ Random (requires nothing) AI TO PLAY GAMES ▸ Togelius
  21. 21. II. CONTRIBUTE CONTENT.
  22. 22. CONTRIBUTE CONTENT PROCEDURAL CONTENT GENERATION
  23. 23. CONTRIBUTE CONTENT PROCEDURAL CONTENT GENERATION
  24. 24. CONTRIBUTE CONTENT PROCEDURAL CONTENT GENERATION
  25. 25. CONTRIBUTE CONTENT PROCEDURAL CONTENT GENERATION • Artistic aspects • Corner-cases • Lack of complete control • Depends on the content • Client-side calculations? • Replayable content? • Cheap • Lots of content • Dynamic Reaction on player • Reduce burden of artist • Save memory • Large worlds • Replayable content • http://pcg.wikidot.com/category-pcg-algorithms
  26. 26. METHODS ▸ Search-Based Methods ▸ Solver-Based Methods ▸ Grammar-Based Methods ▸ Cellular Automata ▸ Noise and Fractals ▸ Machine Learning CONTRIBUTE CONTENT
  27. 27. GENERATE CONTENT FOR… ▸ Environments (Random Maps, Random Dungeons) ▸ Generative Art and models ▸ Textures ▸ Music ▸ Story ▸ Gameplay CONTRIBUTE CONTENT
  28. 28. III. UNDERSTAND PLAYERS
  29. 29. PLAYER MODELING ▸ … detection, prediction and expression of human player characteristics that are manifested through cognitive, affective and behavioral patterns while playing games ▸ can be used to dynamically adjust the gameplay (dynamic difficult adjustment)
  30. 30. BEHAVIOURAL PROFILING
  31. 31. B A R T L E ’ S G A M E R T Y P E S http://www.gamerdna.com/quizzes/bartle-test-of-gamer-psychology
  32. 32. Story Story Enjoyer Party Player Killer Online Hero Allrounder 0% 20% 40% 60% 80% 100% Story Enjoyer Party Player Killer Online Hero Allrounder Time spent Story Campaign Arena Online MulAplayer Local MulAplayer P L AY E R H A B I T ( P L AY E R F I N G E R P R I N T )
  33. 33. P L AY E R P R O F I L E S I N F O R Z A • What Drives People: Creating Engagement Profiles of Players from Game Log Data • 120 mio race entries from 1.2 mil players • Harpstead, E., Zimmermann, T., Nagapan, N., Guajardo, J. J., Cooper, R., Solberg, T., & Greenawalt, D. (2015, October). What Drives People: Creating Engagement Profiles of Players from Game Log Data. In Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play (pp. 369-379). ACM.
  34. 34. F L O W ( M I H A LY C S I K S Z E N T M I H A LY I )
  35. 35. HOW PLAYSTYLES EVOLVE: PROGRESSION ANALYSIS AND PROFILING IN JUST CAUSE 2 https://link.springer.com/chapter/10.1007/978-3-319-46100-7_8
  36. 36. D ATA S E T • Dataset provided by Square Enix • Play histories from over 5000 JC2 players (2010) • Various behavioural features collected: • actions with • in-game geographical coordinates • timestamps • metrics from the gameplay • e.g. total kills, total chaos, kilometres driven # of stronghold takeovers ,… • Data set pre-processing (cleaning): • Outliers removed: scores outside 1-99th percentile excluded • (faulty tracking or errors)
  37. 37. F E AT U R E S • Agency missions (+ reach specific level of Chaos) • subset of features based on the core mechanics • -> does not impact the analytical framework • -> impacts the kinds of conclusions that can be derived
  38. 38. F E AT U R E S • Spatio-temporal navigation • combat performance • progression through the main storyline • side quests.. • Agency missions (+ reach specific level of Chaos) • subset of features based on the core mechanics • -> does not impact the analytical framework • -> impacts the kinds of conclusions that can be derived
  39. 39. P L AY E R P R O G R E S S I O N A L O N G T H E M I S S I O N S
  40. 40. R E S U LT S • How can we describe player behaviour of the different player profiles?
  41. 41. P L AY E R B E H AV I O U R A L O N G T H E S T O RY L I N E jpirker.com/jc2/aaSankey.html
  42. 42. S O C I A L N E T W O R K S I N D E S T I N Y Rattinger, A., Wallner, G., Drachen, A., Pirker, J., & Sifa, R. (2016, September) Integrating and Inspecting Combined Behavioral Profiling and Social Network Models in Destiny,15th International Conference on Entertainment Computing (in press).
  43. 43. NETWORK RELATIONSHIP ‣ Jammer Network ‣ three-year span ‣ v: jammers ‣ e: developed a game together 
 ‣ undirected, weighted graph ‣ (weight: # games developed together) JAMMER 1 JAMMER 2 JAMMER 3 3 1
  44. 44. NETWORK
  45. 45. NETWORK
  46. 46. G O A L S • Improve our understanding of the different player behaviours and factors to improve engagement • Find issues to avoid drop-outs • Provide tools for game designers to (visually) analyse the game and improve the understanding of players • Find game design flaws early and automatically
  47. 47. IV. DESIGN GAMES
  48. 48. AI AS A PART OF GAME DESIGN!!!!
  49. 49. AI TO DESIGN GAMES ROLES OF AI IN GAMES ▸ AI in the foreground of games - Foregrounding AI ▸ create gameplay based around thinking about how agents work ▸ Designing games that use AI techniques in a new way as a core of their gameplay https://medium.com/@mtrc/tombs-of-tomeria-7c2e800a6511 Mike Treanor, Alexander Zook, Mirjam P Eladhari, Julian Togelius, Gillian Smith, Michael Cook, Tommy Thompson, Brian Magerko, John Levine and Adam Smith: AI-Based Game Design Patterns. Computational Creativity and Games Workshop, 2015.
  50. 50. AI-BASED GAME DESIGN ▸ Game design strategies/rules described when AI still “young” and most games are designed to not need AI ▸ Game designers often claim that AI won’t make games better ▸ Our goal: show where AI can be used, show alternative routes ▸ we need to design new games from scratch based on new design principles Mike Treanor, Alexander Zook, Mirjam P Eladhari, Julian Togelius, Gillian Smith, Michael Cook, Tommy Thompson, Brian Magerko, John Levine and Adam Smith: AI-Based Game Design Patterns. Computational Creativity and Games Workshop, 2015. AI TO DESIGN GAMES
  51. 51. AI GAME DESIGN PATTERNS Mike Treanor, Alexander Zook, Mirjam P Eladhari, Julian Togelius, Gillian Smith, Michael Cook, Tommy Thompson, Brian Magerko, John Levine and Adam Smith: AI-Based Game Design Patterns. Computational Creativity and Games Workshop, 2015. AI TO DESIGN GAMES
  52. 52. AI DESIGN PATTERNS 1 AI IS VISUALIZED ▸ Pattern: Provide a visual representation of the underlying AI state, making gameplay revolve around explicit manipulation of the AI state. ▸ Example: Third Eye Crime is a stealth game that illustrates this pattern by visualizing the guard AI position tracking and estimation system. Gameplay involves avoiding guards or throwing distractions to manipulate the guards’ predictions of player location. The direct visualization of AI state allows a designer to build a game around manipulating, understanding, and mentally modeling how the AI state changes.
  53. 53. 2 AI AS ROLE-MODEL ▸ Pattern: Provide one or more AI agents for the player to behave similarly to. ▸ Example: Spy Party is a game where one player is a spy at a party populated by FSM agents and the opposing player is a sniper watching the party with a single shot to kill the spy. Gameplay for the spy centers on the player attempting to act similarly to the party agents while discreetly performing tasks in the environment like planting a bug or reading a code from a book. AI DESIGN PATTERNS
  54. 54. 3 AI AS TRAINEE ▸ Pattern: Have player actions train an AI agent to perform tasks central to gameplay. ▸ Example: Black & White is a god game where the player trains a creature to act as an autonomous assistant in spatial regions where the player cannot take direct action. The creature learns sets of behaviors through a reward signal based on a needs model; the creature also takes direct feedback through player action (e.g., slapping or petting the creature after it takes actions). AI DESIGN PATTERNS
  55. 55. 4 AI IS EDITABLE ▸ Pattern: Have the player directly change elements of an AI agent that is central to gameplay. ▸ Example: Galactic Arms Race is a space shooter where how the player uses different weapons evolves an underlying neural network representation to change weapon firing behavior. Base gameplay revolves around finding a set of firing behaviors that together enable a player to succeed at destroying opposition (another example of the AI as Trainee pattern). One gameplay mode allows the player to explicitly manipulate the network weights on weapons, allowing more precise control over the firing patterns of the evolved weapons. This control enables players to more finely explore the space of parameterizations, leading to an indirect way to understand the processes of the AI system. Erin J. Hastings, Ratan K. Guha, and Kenneth O. Stanley (2009) Automatic Content Generation in the Galactic Arms Race Video Game In: IEEE Transactions on Computational Intelligence and AI in Games, volume 1, number 4, pages 245-263, New York: IEEE Press, 2009. (Manuscript 19 pages) AI DESIGN PATTERNS
  56. 56. 5 AI IS GUIDED ▸ Pattern: The player assists a simple or brittle AI agent that is threatened with self-destruction. ▸ Example: The Sims addressed the problem of “human-like” agents in a social world by making gameplay revolve around the player addressing the needs of simple agents. AI agents have a set of needs and desires they attempt to pursue while players intervene to provide for the needs of the agents through food, shelter, work, socialization, and eventually more grand life aspirations. By having players care for the AI, players come to (at least indirectly) model some of the processes used by the AI. AI DESIGN PATTERNS
  57. 57. 8 AI AS VILLAIN ▸ Pattern: Require players to complete a task or overcome an AI opponent where the AI is aiming to create an experience (e.g., tension or excitement) rather than defeat the player. ▸ Example: Alien: Isolation is a first-person survival horror game where the opposing alien was designed to harass the player without using an optimal strategy that would always kill the player directly. The enemy alien spends the game hunting the player, displaying behaviors of seeking the player’s location (a weak version of AI is Visualized), and gradually learning from tactics the player uses repeatedly (an oppositional application of AI as Trainee). By having players continually reason on what the alien has learned and where it will go the player is forced to consider the state of the AI and (after repeated play) the processes involved in the AI learning. AI DESIGN PATTERNS
  58. 58. AI TO CREATE GAMES!!!!
  59. 59. AI TO DESIGN GAMES / COMPUTATIONAL CREATIVITY
  60. 60. RESOURCES ▸ IEEE Computational Intelligence and Games (CIG) ▸ AAAI Artificial Intelligence in Interactive Digital Entertainment (AIIDE) ▸ Foundations of Digital Games (FDG) ▸ IEEE Transactions on Games (ToG) ▸ Yannakakis and Togelius: Artificial Intelligence and Games www.gameaibook.org
  61. 61. THANK YOU FOR YOUR ATTENTION. JOHANNA PIRKER, JPIRKER@MIT.EDU, @JOEYPRINK 
 Further information: jpirker.com This is how others play your game!

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