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Acquiring & Generating Fun in the Petabyte Age

This AAAI Workshop on AI and Fun explores the challenges of content creation in the era of big data, focusing on the ability to acquire and generate engaging experiences. It discusses the use of tools, planners, and automatic learning to empower non-experts in creating interactive stories and characters. The Restaurant Game is used as an example, showcasing the potential of data-driven games.

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Acquiring & Generating Fun in the Petabyte Age

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  1. Acquiring & Generating Fun in the Petabyte Age AAAI Workshop on AI and . . . Fun? July 11,2010 Jeff Orkin MIT Media Lab Cognitive Machines Group http://www.jorkin.com

  2. “One scripter became two scripters, then three, and then a whole team. […] The scripting team was still badly overloaded and in perpetual crunch.” Clint Hocking, Game Developer Magazine Postmortem of Far Cry 2 Content Authoring Bottleneck

  3. “Not as much has happened in AI as one might have hoped. […] The major reason for this is really one of content. […] We simply were not able to even begin to acquire the content of [human] memory. […] Someone has to get a computer to know what a human knows about using a toaster or playing baseball.” -- Roger Schank

  4. The Petabyte Age

  5. The Petabyte Age

  6. Halo 3 (2007) Issue 15.09. August, 2007 Halo 3: How Microsoft Labs Invented a New Science of Play

  7. The Restaurant Game

  8. The Restaurant Game http://theRestaurantGame.net To Date: • 15,726 unique players • 9,960 game logs

  9. Research Goals • Empower non-experts to create characters who • can play roles in interactive stories. • Characters can communicate and work together • with humans and other AI characters. We are developing: • Tools for non-experts, to author content from data • (by annotating and partitioning). • Planner to render behavior from annotated gameplay • traces, consisting of thousands of actions and utterances.

  10. Automatic Learning and Generation of Social Behavior from Collective Human Gameplay. AAMAS, 2009

  11. Automatic Learning and Generation of Social Behavior from Collective Human Gameplay. AAMAS, 2009

  12. Semi-Automatic Task Recognition for Interactive Narratives with EAT & RUN. 3rdIntelligent Narrative Technologies Workshop, 2010.

  13. AI and Fun http://theRestaurantGame.net Contributing Data Developing Games from Data Playing Data-Driven Games

  14. Fun  Contributing Data

  15. Fun  Developing Games from Data designer Game player is both the author and the audience. “Tolstoy records that shortly after Vronsky made love to Anna Karenina, to the author's great surprise his character began preparing to kill himself. Tolstoy recalled writing on feverishly to find out how the scene would end.” -- Abbott. The Cambridge Introduction to Narrative. 2002.

  16. WAITRESS: Welcome to the restaurant CUSTOMER: Hi WAITRESS: hi, how many in your party? CUSTOMER: a table for one please. WAITRESS: smoking or non? CUSTOMER: WOW, you have a smoking section? CUSTOMER: I don’t smoke, but I will start tonight CUSTOMER: take me there!

  17. WAITRESS: hi CUSTOMER: hi WAITRESS: do you have a reservation? CUSTOMER: I have personal reservations CUSTOMER: but I waive them when it comes to lust.

  18. WAITRESS: welcome CUSTOMER: table for one CUSTOMER: please WAITRESS: have a sit CUSTOMER: have a SEAT

  19. Fun  Playing Data-Driven Games “Players enjoy being subversive to some degree. I think for most people, it’s that the more I control this experience, the better the game is in terms of finding that I can choose to go off into an interesting path, and the game will support that path. It’s really just about empowering the players to not hit walls as often.” -- Will Wright

  20. AI Content Creation == Barrier to Entry Recorded Gameplay + Data Annotation != Barrier to Entry

  21. Mars Escape Sonia Chernova, Jeff Orkin, Cynthia Breazeal

  22. LIFE IS A GAME

  23. http://theRestaurantGame.net Questions? Jeff Orkin http://www.jorkin.com

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