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User Generated AI for Interactive Digital Entertainment 2011

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See http://cognitivecomputing.wordpress.com/2009/09/28/user-generated-ai-for-interactive-digital-entertainment/

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User Generated AI for Interactive Digital Entertainment 2011

  1. 1. User-Generated AIfor Interactive Digital Entertainment Ashwin Ram Cognitive Computing Lab
  2. 2. @ashwinram #scai
  3. 3. CollaboratorsRSs / Postdocs PhD students MS students Undergrads FundingHua Ai Manish Mehta Sanjeet Hajarnis Paritosh Mohan DARPA NSFSanti Ontañón Saurav Sahay Christina Leber Gabriel Cebrian ONR ARLMarco Antonio Denis Aleshin Ken Hartsook Katie Long AFRL AFOSRPedro Pablo Alex Zook Hayley Borck Tom Amundsen GRA NIHJuan Santamaría Toni Barella Kane Bonnette Eric Johnson ARDA (IARPA) Brian Sherwell Anna-Marie Mansour Alistair Jones Disney SAICFaculty David Llanso Jai Rad Christina Lacey Yamaha GMMark Riedl Iulian Radu Rushabh Shah Andrew Trusty Lockheed GECharles Isbell Neha Sugandh Kinshuk Mishra DECMichael Mateas Christina Strong Manu Sharma Peng Zang Dev Priya
  4. 4. Cognitive Computing Cognitive Systems Cognitive Science
  5. 5. Cognitive Computing Intelligent Machines Intelligent Interaction
  6. 6. Recent Projects Play interactive games Control robots Analyze blogs Monitor power plants Help students learn Improve healthcare search …and more 6 OpenStudy.com
  7. 7. OpenStudy.com 7
  8. 8. Massively Multiplayer Online Learning
  9. 9. Games ~$11B market 9
  10. 10. “Games”•  Games are “entering a new era, where technology and creativity will fuse to produce some of the most stunning entertainment of the 21st century.” (Doug Lowenstein)•  “Games are widely used as educational tools, not just for pilots, soldiers and surgeons, but also in schools and businesses.” (The Economist)•  “Serious games” focus on “management and leadership challenges facing the public sector in education, training, health, and public policy.” (www.seriousgames.org)•  “Humane games” include “interactive tools for medical training, educational games, and games that reflect social consciousness or advocate for a cause.” (Scott Leutenneger) 10
  11. 11. Gamers•  Average game player age: 35•  % gamers over age of 50: 25•  % online gamers who are female: 40•  % parents who play games: 35•  % gamer parents who are women: 47•  % gamers who vote: 73•  % gamers who volunteer: 45•  % gamers who exercise or play sports: 79•  % gamers who read regularly: 93•  % gamers who attend concerts, museums, theater: 62 11
  12. 12. Gamers•  Average game player age: 12
  13. 13. Gamers•  Average game player age: 33 13
  14. 14. Gamers•  Average game player age: 33•  % gamers over age of 50: 14
  15. 15. Gamers•  Average game player age: 33•  % gamers over age of 50: 25 15
  16. 16. Gamers•  Average game player age: 33•  % gamers over age of 50: 25•  % online gamers who are female: 16
  17. 17. Gamers•  Average game player age: 33•  % gamers over age of 50: 25•  % online gamers who are female: 42 17
  18. 18. Gamers•  Average game player age: 33•  % gamers over age of 50: 25•  % online gamers who are female: 42•  % parents who play games: 18
  19. 19. Gamers•  Average game player age: 33•  % gamers over age of 50: 25•  % online gamers who are female: 42•  % parents who play games: 35 19
  20. 20. Gamers•  Average game player age: 33•  % gamers over age of 50: 25•  % online gamers who are female: 42•  % parents who play games: 35•  % gamer parents who are women: 20
  21. 21. Gamers•  Average game player age: 33•  % gamers over age of 50: 25•  % online gamers who are female: 42•  % parents who play games: 35•  % gamer parents who are women: 47 21
  22. 22. Gamers•  Average game player age: 33•  % gamers over age of 50: 25•  % online gamers who are female: 42•  % parents who play games: 35•  % gamer parents who are women: 47•  % gamers who vote: 22
  23. 23. Gamers•  Average game player age: 33•  % gamers over age of 50: 25•  % online gamers who are female: 42•  % parents who play games: 35•  % gamer parents who are women: 47•  % gamers who vote: 73 23
  24. 24. Gamers•  Average game player age: 33•  % gamers over age of 50: 25•  % online gamers who are female: 42•  % parents who play games: 35•  % gamer parents who are women: 47•  % gamers who vote: 73•  % gamers who volunteer: 45•  % gamers who exercise or play sports: 79•  % gamers who read regularly: 93•  % gamers who attend concerts, museums, theater: 62 24
  25. 25. Real-Time Strategy Games HomeWorld Age of Empires
  26. 26. Interactive Fiction Zork Choose Your (1977)! Own Adventure (Books - 1979)! Monkey Island (1990)! And Then There Were None (2005)!
  27. 27. Immersive Virtual Worlds Hans Christian Andersen •  Target group: kids & youngsters •  Input: 2D gestures on touch screen, free speech •  Output: 3D graphics virtual world, animated embodied conversational agents, interactive speech and gestural behavior 27
  28. 28. Social Gaming ~$2B market 12M users $100M market cap $1B virtual goods economy 60 90 200M users + 20M + 20M + … ~$1.60 ARPU $400M acquisition 28
  29. 29. Social Gaming ~$2B market Consumers 12M users $100M market cap Participants 60 90 200M users $1B virtual goods economy + 20M + 20M + … Creators $400M acquisition ~$1.60 ARPU 29
  30. 30. Worldwide market •  $80 million in 2006 •  $1.1 billion in 2007 •  ~$13 billion by 2011More than $7.3 billionby 2013 Juniper  Research  
  31. 31. IMVU: 1.6 millionuser generatedvirtual itemsTeaching gameprogrammingto 3rd graders:Scratch
  32. 32. Users  can  create  simple  games   But…   Cannot  create     intelligent  behaviors  Users  can  create  physical  ar/facts   But…   Not a Robot Cannot  create     interes/ng  personali/es  
  33. 33. AI is missing from the social media landscape 33
  34. 34. Towards “AI 2.0”• Problem: –  User-created content is everywhere: photos, videos, news, blogs, virtual goods, avatars, games… …but not AI• Vision: –  Allow end-users to build AIs –  Provide a new kind of social gaming experience• Solutions: –  Intelligent Editors –  Meta-Reasoning & Adaptation –  Case-Based Learning
  35. 35. Game AI Wargus
  36. 36. What is Game AI?•  AI powers the game characters –  “Believable agents” with complex behaviors –  Focused on NPC’s own goals –  Embedded in the game 36
  37. 37. AI for NPCs Tag
 Wargus! ! (Unreal) 37
  38. 38. What is Game AI?•  AI powers the game characters –  “Believable agents” with complex behaviors –  Focused on NPC’s own goals –  Embedded in the game•  AI powers the game director –  “Drama manager” with global perspective –  Focused on author’s rhetorical and affective goals –  Watches over the game-player interaction –  Enhances player experience 38
  39. 39. AI for DM GOAL Guide the player to a more enjoyable experience" That silver locket Anchorhead
 (1998)! looks curious! Game ! Player! Player ! Engine! Trace! Modeling! Player! DM! Player! actions! Model! Physical! Story! state! state! Drama History! Manager ! Game State! 39
  40. 40. Why is Game AI hard?•  Huge Decision Space (thousands of possible states and actions)•  Cognitive Modeling (goals, strategies, plans, tactics, behaviors, personalities, teams)•  Non-Deterministic and Real-Time•  Classical approaches don’t work directly Aha et al (ICCBR-05)
  41. 41. Games are AI-complete•  “Games require players to construct hypothesis, solve problems, develop strategies, learn the rules of a new world through trial and error.”•  “Gamers must be able to juggle several different tasks, evaluate risks, and make quick decisions.” (The Economist) Well then, so must Game AI ! 41
  42. 42. Game AI is hard …even for experts parallel behavior AttackEnemy() { context_condition { Theory Engineering e = (UnitWME Visibility == VISIBLE) } act attack(e); subgoal KeepUnitsEngaged(); subgoal ConcentrateFire(e);How to model } Façade hasrealistic, human-level sequential behavior HealUnit() { precondition { 200,000+ lines of ABL code for 2 (UnitWME health < CRITICAL unitID::target personalities? location::tcoord) NPCs (UnitWME canHeal==true mana > 0 intentions? unitID::healer location::hcoord spellrange::range) behaviors? } (tcoord.distanceto(hcoord) < range) act castspell(HEAL, healer, target); language? } Hans Christian … sequential behavior ProtectBase (){ Andersen: precondition { AI: 14 person-yearsin large-scale attackbeh = (GoalStepWME signature == “AttackEnemyBase()”) game/graphics: 9non-deterministic } (BaseIsUnderAttack) gesture: 4real-time worlds? mental_act{ attackbeh.fail(); speech: 1 Problem: How will end-users create AIs? } } 42
  43. 43. Case-Based Reasoning Problem Retrieve Past Cases Learn Case Adapt New Library Case Tentative Solutions Solution Repair
  44. 44. Research IssuesCBR for Game AI•  Games require online planning & execution•  Cases must be retrieved, adapted, combined, and repaired on the fly•  Effects of actions depend on other player’s actions•  Solutions to problems are partial plans that are: – created strategically HarvestGold(peasant1,gold_mine1) – expanded reactively Train(“peasant”) Build(peasant2,”barracks”,15,20) Train(“knight”) Attack(knight1,enemy_tower1)•  Where do the cases come from?
  45. 45. User-Generated AI…build a “mind” without programming Users create behaviors visually System fixes “bugs” during behavior execution User “programs” behaviors by demonstration 45
  46. 46. User-Generated AI…build a “mind” without programming Behavior authoring Behavior adaptation Behavior demonstration 46
  47. 47. User-Generated AI…build a “mind” without programming Behavior authoring Behavior adaptation Behavior demonstration 47
  48. 48. 1) Behavior Authoring…Second Mind Updates… 1 Login to Virtual World 2 Choose a 48 Scene 3 Hey!! Wanna play. I am looking Choose a Character SMPlayer Flirting with girl at bar for a partner. Christina Teach your avatar how will you flirt … Flirt with Girl at Posted By: 2MGuru *** Flirting with girl I bar n More Join at bar v Teach your avatar how will you i t flirt … Get coffee spilled on you e SMPlayer Posted By: 2MGuru *** Show how will you react if coffee … Acti 2MGuru Posted By: 2MGuru *** SMPlayer Flirt with Girl at vate 2MGuru 2MGuru bar More Join Flirt with Girl Fool your boss Fool your boss Imagine how many ways you can Imagine how many ways you can … … Posted By: 2MGuru *** Posted By: 2MGuru *** Act In iva vi te te More Join Sure! Lets find a suitable location… 4 Author your Behaviors Role play your 5 Play your Scene 6 Save in Shared Library Great! Hi. How scene How More  op/ons…  MYScene:   My   SCENE: Hi about are Impress  a  Date  Flirt with a girl H ow you … you 00 Greet :0 ar e doing Flirt Impress 0   thi ng s … go in g 0 00:00 Hi! How are walks closer 0 you? : 0 Not at all! 0   Sure… Behaviors Stories Do you mind Hi How are things if I join you? … going Personalities I am doing great
  49. 49. Add a goal
  50. 50. Add a goal
  51. 51. Add a goal
  52. 52. Add an action
  53. 53. Add an action
  54. 54. Add an action
  55. 55. Add an action
  56. 56. Add an action
  57. 57. Add an action
  58. 58. Add an action
  59. 59. Group actions with goals
  60. 60. Group actions with goals
  61. 61. Add goals of others
  62. 62. Add goals of others
  63. 63. Add goal details
  64. 64. Add goal details
  65. 65. Add goal details
  66. 66. Second Mind other minds Other’s Sensory Your goal goal info Sensory? Goals   00:0 Question 0   Answer Pleased How   Ac8ons   much  is   This   00:the   vase  is   00  vase  for   …………   67 Goal: Greet a customer  
  67. 67. User-Generated AI…build a “mind” without programming Behavior editing Behavior adaptation Behavior learning 68
  68. 68. 2) Behavior AdaptationHand-authored character is not quite right How to detectbreak in userexperience? Tag
 ! (Unreal) 69
  69. 69. Meta Reasoning (Reflection) BehaviorGoals Pattern Failure Library Mod-Ops Matching Patterns Intelligent Abstracted NPC Trace Reasoning TraceGame Trace EvaluationEnvironment
  70. 70. Failure Patterns• Encode “faulty” behavior execution traces• Associated with “mod ops” !x !FAIL !x FAIL ?x START !x ANY !x START Within MINTIME !x FAIL After MINFAILsx represents abehavior
  71. 71. Turn CBR on itself Execution Trace 1 Beh “Talk User doesn’t User walks User leaves Beh 1 about chair” respond to around and the started didn’t get avatar comes back conversation triggered User asks X Beh 3 No No User User arrives finishes Interaction Options left for X min Listener Enthusiastic User Feedback 3 2 2 5 0 Execution Trace 2 Beh. “Talk User walks User User asks Y Beh 1 about teek- around and express started chair” gets comes back thanks triggered Beh 3 User says Y Avatar talks User leaves the User arrives User asks X finishes about user’s conversation visit Listener Enthusiastic User Feedback 2 2 4 5 4
  72. 72. Traces = Cases (of Reasoning)
  73. 73. Meta-CBR cycle1 2 3 CBR Feedback Trace Analysis 6 4 5 Behavior Repair Trace Difference Trace Base
  74. 74. AIs that repair themselves 75
  75. 75. User-Generated AI…build a “mind” without programming Behavior editing Behavior debugging Behavior demonstration 76
  76. 76. 3) Behavior Demonstration Make it even easier: •  User demonstrates how the AI should behave •  Systems builds the AI automatically Key technology: •  Real-Time CBR
  77. 77. Darmok 2•  Real-Time Case-Based Planning Learning system•  Designed to play RTS games, but domain independent•  Automatically Learns Cases from Demonstration•  Available on SourceForge
  78. 78. Darmok 2: Overview Case-Based Plan Game Planning Execution Case Base Case-Based Demonstrations Learning Planning Learning Data Handcrafted Data
  79. 79. Darmok 2: Plan Representation•  Plans represented as Hierarchical Petri Nets: –  Sequential –  Parallel –  Conditionals –  Loops –  Primitive Actions –  Subgoals (hierarchy)•  Plans learned automatically from observations•  Petri Nets are very expressive –  Darmok 2 is limited to what the learning module can produce (e.g. currently no loops)
  80. 80. Darmok 2: Case Representation Snippet 1: Episode 1: GOAL: 0 Timeout(500) S0: 1 Wood300 STATE: Gold400 gamestate entity id=“E14“ type = “Player” Train gold1200/gold 0 !Exists(E4) S1: 0 wood1000/wood (E4,”peasant”) ownerplayer1/owner /entity NewUnitBy(U4) entity id=“E15“ type = “Player” gold1200/gold wood1000/wood 0 Timeout(500) S2: 0 ownerplayer2/owner /entity entity id=“E4“ type = “Townhall” ExistsPath(E5,(17,18)) x6/x y0/y ownerplayer1/owner Harvest(E5, 0 !Exists(E5) S3: 0 hitpoints2400/hitpoints (17,18)) /entity … Status(E5)==“harvest” /gamestate S4: 0 OUTCOME: 1.0
  81. 81. Darmok 2: Architecture Adversarial Plan Case Planner Real-Time Plan Retrieval Minimax Execution Game State Plan Plan Simulation Adaptation Case Base Opponents Actions Game Model Model Opponents Case Model Learning Learning Demonstrations Planning Learning Data Handcrafted Data
  82. 82. Darmok 2: Planner Adversarial Plan Case Planner Real-Time Plan Retrieval Minimax Execution Game State Plan Plan Simulation Adaptation Case Base Opponents Actions Game Model Model Opponents Case Model Learning Learning Demonstrations Planning Learning Data Handcrafted Data
  83. 83. Darmok 2: Learner Adversarial Plan Case Planner Real-Time Plan Retrieval Minimax Execution Game State Plan Plan Simulation Adaptation Case Base Opponents Actions Game Model Model Opponents Case Model Learning Learning Demonstrations Planning Learning Data Handcrafted Data
  84. 84. Learning Plans from Demonstration• Input: Human – Demonstration (trace) Game – Goal Ontology Demonstration• Output: – Plans and Cases Goals Plan Learning Case Base
  85. 85. Representing Demonstrations Time Stamp Game State Actions 0 GS0 Player 2: Build(U4,”Farm”,28,22) 5 GS5 Player 1: Build(U2,”Farm”,7,21) 35 GS35 Player 2: Train(U3,”Peasant”) 56 GS56 Player 1: Train(U1,”Peasant”) 123 GS123 Player 2: Harvest(U5,29,0) 160 GS160 Player 1: Build(U6,”Farm”,9,21) …
  86. 86. The Goal Matrix Demonstration G1 G2 G3 G4 G5 t1,GS1,A1 0 0 0 0 0 t2,GS2,A2 0 0 0 0 0 t3,GS3,A3 0 0 0 0 0 t4,GS4,A4 0 0 0 0 0 t5,GS5,A5 0 0 0 1 0 t6,GS6,A6 0 0 1 1 0 t7,GS7,A7 0 1 1 1 0 t8,GS8,A8 0 1 1 1 1 t9,GS9,A9 0 1 1 1 1 t10,GS10,A10 0 1 1 1 1 t11,GS11,A11 0 1 1 1 1 t12,GS12,- 1 1 1 1 1
  87. 87. The Goal Matrix: Linear Plans Demonstration G1 G2 G3 G4 G5 t1,GS1,A1 0 0 0 0 0 t2,GS2,A2 0 0 0 0 0 t3,GS3,A3 0 0 0 0 0 t4,GS4,A4 0 0 0 0 0 Actions A1 … t5,GS5,A5 0 0 0 1 0 A11 managed t6,GS6,A6 0 0 1 1 0 to make goal t7,GS7,A7 0 1 1 1 0 G1 true t8,GS8,A8 0 1 1 1 1 starting from t9,GS9,A9 0 1 1 1 1 game state t10,GS10,A10 0 1 1 1 1 GS1 t11,GS11,A11 0 1 1 1 1 t12,GS12,- 1 1 1 1 1
  88. 88. Plan Dependency Graph Plan A1.- Harvest(U2,(0,16)) 2 A2.- Train(U4,”peasant”) A3.- Harvest(U3,(17,23)) 1 3 4 A4.- Train(U4,”peasant”) A5.- Build(U5,”LumberMill”,(4,23)) 5 6 8 A6.- Build(U5,”Barracks”,(8,22)) A7.- Train(U6,”archer”) A8.- Build(U5,”tower”) 7 9 A9.- Train(U6,”archer”) A10.- Attack(U7,EU1) 10 11 A11.- Attack(U8,EU2)
  89. 89. Hierarchical Composition of PlansDemonstration G1 G2 t1,GS1,A1 0 0 t2,GS2,A2 0 0 2 t3,GS3,A3 0 0 t4,GS4,A4 0 0 1 3 4 G2 t5,GS5,A5 0 0 t6,GS6,A6 0 0 5 6 8 9 8 t7,GS7,A7 0 1 t8,GS8,A8 0 1 7 9 10 11 t9,GS9,A9 0 1t10,GS10,A10 0 1 10 11t11,GS11,A11 0 1 t12,GS12,- 1 1
  90. 90. Case Construction• From each plan extracted from the goal matrix, a case is created: Snippet 1: Episode 1: GOAL: G1 G2 STATE: 9 8 gamestate id=“GS1” … /gamestate 10 11 OUTCOME: 1.0
  91. 91. Case = User Generated AI Snippet 1: Episode 1: GOAL: 0 Timeout(500) S0: 1 Wood300 STATE: Gold400 gamestate entity id=“E14“ type = “Player” Train gold1200/gold 0 !Exists(E4) S1: 0 wood1000/wood (E4,”peasant”) ownerplayer1/owner /entity NewUnitBy(U4) entity id=“E15“ type = “Player” gold1200/gold wood1000/wood 0 Timeout(500) S2: 0 ownerplayer2/owner /entity entity id=“E4“ type = “Townhall” ExistsPath(E5,(17,18)) x6/x y0/y ownerplayer1/owner Harvest(E5, 0 !Exists(E5) S3: 0 hitpoints2400/hitpoints (17,18)) /entity … Status(E5)==“harvest” /gamestate S4: 0 OUTCOME: 1.0
  92. 92. Case Library Demonstration (trace) Demonstration (trace) Demonstration (trace) Snippet Episode Snippet Episode Plan Goals Learning Snippet Episode Snippet Episode Snippet Episode
  93. 93. Summary:Case Learning Process Demonstration Demonstration Game Goal Matrix Goals Goal Matrix Generation Plan Dependency Hierarchical Hierarchical Graphs Composition Plans Case Construction
  94. 94. HarvestGold(peasant1,gold_mine1)Case Retrieval Train(“peasant”) Build(peasant2,”barracks”,15,20)•  Heterogeneous Train(“knight”) Attack(knight1,enemy_tower1) Case Base•  Similarity depends on both Goal and Game State Goal Current New Problem Game StateSnippet Episode Snippet Episode Goal Similarity Snippet Episode Aggregation Similarity Snippet Episode Game State SimilaritySnippet Episode
  95. 95. Real-Time Reactive PlanningReasoner uses case-based functionapproximator SM( E, C, p) = J min( p,lE ) (Einput j (i) − Cinput j ( p − i)) 2 ∑w ∑ j p +1 j=1 i= 0 K min( p,l E ) ( Eoutput k (i) − Coutput k ( p − i)) 2 + ∑ wk ∑ k =1 i −0 p +1Learner modifies • Case library • Input parameters • Action policies Integrates CBR withSVM and TD-learning ΔQi = α ( rt + 1 + γ Qt + 1 + Qt ) ei ∀Ci∈memory 96
  96. 96. Multi-Plan CBRReasoner splicessnippets of previousplans to solve newproblems Learner adapts andremembers newsolutions for futureuse Integrates CBR withHTN planning 97
  97. 97. Real-Time Minimax Planner•  Adversarial 1%23+45+65,63,65-7 !#$% 8!99:; ()%)*•  Plans adapted in real-time (#@A%)*•  Interleaved planning and (#@A%)* (#@A%)* (- execution 1%23+45+65,63,65-7 1%23+45+65,63,65-7 1%23+45+65,63,65-7 (, (+ 85=9:; 1%BCD3,25.65/ 1%BCD3,2506565? 1%23+45+65,63,45.65/765-7 (+ (+ 1%23+45+65,63,4506565?765-7 8!99:; (#@A%)* (#@A%)* (#@A%)* (/ (. (0
  98. 98. Test Domains S2 Towers BattleCity•  Simplified Warcraft 2 •  Defense Game • Fast Paced Action•  Important factors: •  Important factors: • Important factors: •  Terrain •  Build order •  Reflexes •  Resources •  Spatial formation •  Navigation •  Buildings •  Units
  99. 99. Experimental Results 100.00%Towers 90.00% 80.00% 70.00% 60.00% BCS2 Towers 50.00% S2 Linear(BC) 40.00% Linear(Towers)Battle 30.00% Linear(S2)City 20.00% 10.00% 0.00% 1 2 3 4 5 6 7 8 9 10 Each point is average of 50 games over 5 different maps
  100. 100. TMMake ME Play ME ME = Mind Engine•  Social Gaming website powered by Darmok 2•  Players create MEs which can play games•  Players can challenge MEs created by other players•  First product to allow TM end-users to create sophisticated AIs
  101. 101. MakeMEPlayME.com
  102. 102. makemeplayme.comsourceforge.net/projects/darmok2secondmind.org 103
  103. 103. User-Generated AI…build a “mind” without programming Users create behaviors Visual visually Editor System fixes “bugs” during Meta- execution Reasoning Case- User “programs” behaviors by demonstration Based Learning 104
  104. 104. Summary• Make ME Play ME is a social gaming website powered by CBR• CBR allows players to create their own MEs without expertise in AI or programming• CBR allows a new kind of gaming experience, not available before
  105. 105. Future Work• Research: Better AI – Works well for some games, not enough for others – Integrate authoring and adaptation techniques• Development: Expand to more games – Easy for end users to add new games• Dissemination: Open source  – Darmok 2 as a core Real-Time CBR system – Make ME Play ME as an AI testbed
  106. 106. Summary…RT-CBR enables User-Generated AI Behavior Behavior Behavior authoring adaptation demonstration Build a “mind” without programming 107
  107. 107. Current Projects AI 2.0 = User Generated AI 108
  108. 108. Funded by NSFMake your own Mind Engines
  109. 109. Funded by Disney Make your own Second Mind other minds Other’s Sensory Your goal goal info Sensory? Second Life Goals   00:0 Question 0   Answer Pleased How   Ac8ons   much  is   This   00:the   vase  is   00  vase  for   …………   110 Goal: Greet a customer  
  110. 110. With Mark Riedl / Funded by DARPAMake your own Interactive Stories Don’t tell me, SHOW ME Lorem! Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Lorem ipsum dolor sit amet. Lorem ipsumLorem ipsum dolor sit amet, consectetur Proin consequat imperdiet dolor sit amet. Lorem ipsum dolor sit amet, consecteturadipiscing elit. Proin consequat imperdiet tincidunt. Aliquam blandit,. adipiscing elit. Proin consequat imperdiettincidunt. Aliquam blandit, tincidunt. 111
  111. 111. OpenStudy.com 112
  112. 112. Massively Multiplayer Online Learning
  113. 113. 9 months of OpenStudyOCW OER Community•  MIT OCW •  CDC (TRAIN) •  50,000 users•  Open Yale Courses •  SmartHistory (MOMA) •  500 “superheroes”•  OpenMichigan •  Purple Math •  600,000 views/month•  UC Irvine •  Tutorial.Math.Lamar.edu •  18 minutes/session•  Notre Dame •  FreeMathHelp •  1,000 questions/day•  NYU •  HelpWithFractions •  70% answered in 5 min•  Emory •  BiologyCorner•  Georgia State U •  CellsAlive•  Korea University •  Physics 1728.com•  NCTU •  HyperPhysics •  Study groups •  Math•  Tufts •  CalculatorSoup •  Biology•  TU Delft •  Videolectures.net •  Physics•  Tokyo University •  Connexions •  Chemistry•  Process.Arts •  DavidDarling Info •  Comm and Media•  African Virtual University •  HippoCampus •  Engineering•  Distance Education Univ. •  Prof. Damodaran •  Computer Science Costa Rica•  Monterey Inst. Technology•  Purdue Online Writing
  114. 114. Back to the FutureAI
  115. 115. Back to the FutureAI The Turing Test
  116. 116. Contactwww.cc.gatech.edu/~ashwin ashwin@cc.gatech.eduwww.linkedin.com/in/ashwinram @ashwinram 117

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