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Ml in games intel game developer presentation v1.2


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This is a presentation I gave at the intel buzz workship seattle June 22nd 2016 #intelgamedev #buzzworkshop #gamedev #intelbuzz
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Ml in games intel game developer presentation v1.2

  1. 1. George Dolbier CTO Interactive Media IBM
  2. 2. What is Machine Learning? Software that Learns and Produces Without Explicit Programming
  3. 3. ASK DISCOVEREXPLORE DECIDE VISUALIZE Ask questions for greater insight Natural language dialogue & robotics Image Video Audio spam OCR Evidence-based decisions with with traceability Consolidate and visualize Loci of Machine Learning Try this for fun Google “Personality Insights Demo” or use this QR code to go to
  4. 4. Machine Learning in Games Creating better play experience 4 Case Study
  5. 5. Balance and Engagement One of the hardest problems in game design is Play Balance. Poor progression ramps, too steep, too shallow, awkward jumps, sited as #1 factor in dis-engagement Questions posed to the team: • Can an ML be used to balance the play experience? • How can you tailor an experience for a specific player? • Can the individual’s telemetry data be used to make better balance decisions? • Can Curated content be delivered to players programmatically to produce a better overall experience?
  6. 6. Case Study : Plight of the Zombie Situation: • Simple puzzle game • Dozens of short 30 to 90 second experiences • Designed by level designers • Each level given a “curated/seeded” difficulty For this initial use case 3 variables are considered: 1. Time it took player to complete level 2. Number of retries the player took to complete level 3. Curated difficulty metric assigned to each level John O’Neil: Sparkplug Games
  7. 7. How tradeoff is used to balance the game Show Watson Tradeoff call
  8. 8. Result Player plays through tutorial levels, and seeded “easy” levels 3 Tutorial levels, 2 “easy” levels By the 5 level enough data has been collected for Watson to begin suggesting ramp in difficulty
  9. 9. Next Steps For this simple use case next obvious steps will be: Use all player telemetry to correct difficulty settings Have Watson identify play patterns in player base Have Watson tell developers progression, engagement, trends − How long to players play − Are players playing longer, shorter − What is triggering in game purchase or conversation Have Watson automatically generate freebies or offers based on struggle
  10. 10. Machine Learning in Games Truly Interactive NPC 10 Case Study
  11. 11. Real time interaction with a fictional character A Long time desire in the industry Can you create a character, that a human can interact with? Can you have a conversation with a character that includes A Backstory An Attitude Likes, Dislikes Conversation Not just scripted QnA tree
  12. 12. The Suspect - Immersive chat thriller Second screen app with synchronised news alerts and live 3D brain scan Main screen with dynamic video, AI chat powered by Watson, gamified experience, and transmedia storyline Gamified conversation with simulated points, level and rank Contextually-served video to match suspect’s responses
  13. 13. Case Study : The Suspect Can we “Throw everything we can” into a character And through natural conversation, bring the player into the character’s world 3 Types of information make up the personality 1.Mind Map 2.Traditional Q and A (Word) 3.Conversation loops (how does character react to repeated questions) 8 to 10 people total, core team maxed at 6, calendar time 18 months Core team focused development 4 months Guy Gadney: Lead Developer for “The Suspect”
  14. 14. Use of Conversational technology Conversational chat bot associated with Brazilian TV show Average session was 20 minutes 8% of chats lasted for over an hour (Target Audience) Site traffic increased 15%. This lead to an increase in advertising revenue around the project's pages.
  15. 15. But Wait! There is More: Alpha Go! Project Aries Guy Gandy HowWeGetToNext article on chatbots Medical Minecraft Fashion Design Google machine learning recipes : Mari/o Using evolution to train neural networks Artomatix ML for texturing
  16. 16. @NoirTalon