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AI in Digital Entertainment

AI in Digital Entertainment

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AI in Digital Entertainment

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  1. AI in Digital Entertainment • Instructor: Rand Waltzman • E-mail: • Phone: 790 6882 • Room: 1430, Lindstedtsvägen 3 • 4 point course • Periods I and II

  2. Administrivia • There is notenta for the course! • There is a final paper. • Design and analysis of some type of digital based entertainment that uses some type of AI technology to enhance the participants experience. • Three homework assignments. • Final paper is required to pass the course. • Final grade will depend on how many successful (graded on a pass/fail basis) homework assignments you hand in on time. • 1 assignment  Final grade 3 • 2 assignments  Final grade 4 • 3 assignments  Final grade 5 • Details of the paper and the homework assignments will be found on the course web site.

  3. “The holy grail of game design is to make a game where the challenges are never ending, the skills required are varied, and the difficulty curve is perfect and adjusts itself to exactly our skill level. Someone did this already, though, and its not always fun. It’s called life. Maybe you’ve played it?”

  4. “The problem with people isn’t that they work to undermine games and make them boring. That’s the natural course of events. The real problem with people is that ... even though our brains feed us drugs to keep us learning ... ... even though from earliest childhood we are trained to learn through play ... ... even though our brains send incredibly clear feedback that we should learn throughout our lives ...” PEOPLE ARE LAZY

  5. New Possibilities • Application of AI techniques offer potential for new: • Media • Design field • Art form • Different dimensions to consider: • Cognitive psychology • Computer science • Environmental design • Storytelling

  6. What is Fun? • A source of enjoyment. • All about making the brain feel good. • Release of endorphins into your system. • Same sorts of chemicals released by • Listening to music we resonate to. • Reading a great book. • Snorting cocaine. • Having an orgasm. • Eating chocolate. • Fun is the feedback the brain gives us when we are absorbing patterns for learning purposes.

  7. Subtle Approach • One of the subtlest releases of chemicals is at the moment of triumph when we • Learn something • Master a task • Our bodies way of rewarding us • This is one of the most important ways we find pleasure in games. • In games, learning is the drug. • Boredom is the opposite. • When the game stops teaching us, we feel bored.

  8. Experience vs. Data • New data is used to flesh out a pattern. • New experience might force a whole new system on the brain. • Potentially disruptive and not so much fun. • Games must continually navigate between • Deprivation vs. overload • Excessive chaos vs. excessive order • Silence vs. noise

  9. How to Make a Boring Game • Player figures out whole game in first 5 minutes. • Player might see that there are incredible number of possible permutations. • Require mastery of a ton of uninteresting details. • Player fails to see any pattern whatsoever. • Pacing of the revelation of variations in the pattern too slow. • Or too fast. • Player masters everything in the available patterns.

  10. A Little Cognitive Theory • The brain is made to fill in the blanks. • E.g., see a face in a bunch of cartoony lines and interpret subtle emotions from them. • Fantastic ability to make and apply assumptions. • The brain is good at cutting out the irrelevant. • Show somebody a movie with a lot of jugglers in it. • Tell them in advance to count all the jugglers. • They will probably miss the large pink gorilla in the background. • The brain notices a lot more than we think. • Put somebody in a hypnotic trance and ask them to describe something vs. • Asking them on the street!

  11. A Little More ... • The brain is actively hiding the real world from us. • Ask somebody to draw something. • More likely to get the generalized iconic version of the object ... • The one they keep in their head. • Rather than the actual object they have in front of them. • Seeing what is actually in front of us is hard. • Most of us never learn how to do it.

  12. Chunking • Compiling an action or set of actions into a routine. • Allows us to perform the action on autopilot. • Burning a recipe into the neurons. • Example: Describe how you get to work in the morning. • Get up • Stumble to the bathroom • Take a shower • Get dressed • Drive to work. • Easy enough, but ...

  13. Chunking • What if I ask you to describe one of these steps? • Example: Getting dressed. • Tops or bottoms first? • Socks in top or second drawer? • Which pant leg goes in first? • Which hand touches the button of your shirt first? • You could probably answer with enoughthought. • This operation has been chunked. • You would have to decompile and that would take time.

  14. More on Chunking ... • We usually run on chunked patterns. • Most of what we see is a chunked pattern. • We rarely look at the real world. • We usually recognize something chunked and leave it at that. • When something in a chunk does not behave as we expect we have problems. • A car starts moving sideways on a road instead of forward. • We no longer have a rapid response. • Unfortunately, conscious thought is very inefficient. • If you have to think about what you are doing, you are likely to screw it up.

  15. 3 Levels of Thought • Conscious thought. • Logical • Works on a basically mathematical level. • Assigns values and makes lists. • Very slow! • Integrative, associative and intuitive. • Non-thinking thought. • You stick your hand in a fire. • You pull it out before you have time to think about it.

  16. Integrative Thought • Part of the brain that does the chunking. • Can’t normally access this part of the brain directly. • It is frequently wrong. • It is the source of common sense. • Often self-contradictory. • “look before you leap” • “he who hesitates is lost” • This is where approximations of reality are built.

  17. Appeal to Their Intelligences • Some basic types of intelligence that entertainment can appealto: • Linguistic • Logical-Mathematical • Bodily-Kinesthetic • Spatial • Musical • Interpersonal • Intrapersonal • Internally directed • Self motivated

  18. Fun is Educational • Learn to calculate odds. • Prediction of events. • Qualitative probability. • Learn about power and status. • Not surprisingly of interest since we are basically hierarchical and strongly tribal primates. • Learn to examine environment or space around us. • Spatial relationships are critically important. • Classifying, collating and exercising power over the contents of space is crucial element of many games. • Using spatial relations as basis for predictive models.

  19. Fun is Educational ... • Learn to explore conceptual spaces. • Understanding rules is not enough. • To exercise power over a conceptual space we need to know how it reacts to change. • Exploring a possibility space is an excellent way to learn about it. • Memory plays an essential role. • E.g., recalling and managing very long and complex chains of information. • Provide tools for exploration. But, the trick is to strike a balance between • Teaching players to rely on tools to overcome their own limitations VS • Making people so dependent on tools that they can’t function without them.

  20. Fun is Educational ... • Learn basic skills: • Quick reaction time. • Tactical Awareness • Assessing the weakness of an opponent. • Judging when to strike. • Network building. • A very modern skill. • As opposed to basic cave-man skills.

  21. Good Entertainment • Thought provoking • Revelatory • Good portrayal of human condition • Provides insight • Contributes to betterment of society. • Forces us to reexamine assumptions. • Gives us different experiences each time we participate. • Allows each of us to approach it in his/her own way. • Forgives misinterpretations • Maybe even encourages them • Does not dictate. • Immerses and imposes a world view.

  22. From Game to Art • For games to reach art, the mechanics must be revelatory of the human condition. • Create games where the formal mechanics are about climbing a ladder of success. • E.g., mechanics simulate not only the projection of power, but concepts like duty, love, honor, responsibility. • Create games that are about the loneliness of being at the top. • Sample Titles • Hamlet: The Game • Working for the Man • Sim Ghandi • Against Racisim • Custody Battle

  23. Example • Your goal is the overall survival of your tribe. • You gain power to act based on how many people you control. • You gain power to heal yourself based on how many friends you have • Friends tend to fall away as you gain power. • So: • Being at the top and having no allies is a choice. • Being lower in the status hierarchy is also a choice • Perhaps more effective • Feedback: • Reward players for sacrificing themselves for the good of the tribe. • If they are captured during the game, they may no longer act directly but still score points based on the actions of the players they used to rule. • This could represent their legacy.

  24. What is Artificial Intelligence

  25. Can Machines Have Minds?

  26. Two Types of Goals

  27. AI and Computer Science

  28. Examples of AI Research

  29. Other AI Research Areas

  30. AI is Inherently Multi-Disciplinary

  31. Different Strokes for Different AI Folks

  32. AI Programming

  33. ACM Computing Classification I.2.0 General Cognitive simulation Philosophical foundations I.2.1 Applications and Expert Systems Cartography Games Industrial automation Law Medicine and science Natural language interfaces Office automation I.2.2 Automatic Programming Automatic analysis of algorithms Program modification Program synthesis Program transformation Program verification

  34. ACM Computing Classification • I.2.3 Deduction and Theorem Proving • Answer/reason extraction • Deduction (e.g., natural, rule-based) • Inference engines      • Logic programming • Mathematical induction • Metatheory • Nonmonotonic reasoning and belief revision • Resolution • Uncertainty, ``fuzzy,'' and probabilistic reasoning

  35. ACM Computing Classification • I.2.4 Knowledge Representation Formalisms and Methods • Frames and scripts • Modal logic      • Predicate logic • Relation systems • Representation languages • Representations (procedural and rule-based) • Semantic networks • Temporal logic      • I.2.5 Programming Languages and Software • Expert system tools and techniques

  36. ACM Computing Classification I.2.6 Learning Analogies Concept learning Connectionism and neural nets Induction Knowledge acquisition Language acquisition Parameter learning

  37. ACM Computing Classification I.2.7 Natural Language Processing Discourse Language generation Language models Language parsing and understanding Machine translation Speech recognition and synthesis Text analysis

  38. ACM Computing Classification • I.2.8 Problem Solving, Control Methods, and Search • Backtracking • Control theory      • Dynamic programming • Graph and tree search strategies • Heuristic methods • Plan execution, formation, and generation • Scheduling     

  39. ACM Computing Classification • I.2.9 Robotics • Autonomous vehicles      • Commercial robots and applications      • Kinematics and dynamics      • Manipulators • Operator interfaces      • Propelling mechanisms • Sensors • Workcell organization and planning

  40. ACM Computing Classification • I.2.10 Vision and Scene Understanding • 3D/stereo scene analysis      • Architecture and control structures • Intensity, color, photometry, and thresholding • Modeling and recovery of physical attributes • Motion • Perceptual reasoning • Representations, data structures, and transforms • Shape • Texture • Video analysis     

  41. ACM Computing Classification • I.2.11 Distributed Artificial Intelligence • Coherence and coordination • Intelligent agents      • Languages and structures • Multiagent systems     

  42. Quality bars of the near-future • Procedurally generated content • “Emergent” behaviors, collisions • Believable characters • 100x physics • Portable avatars, persistent assets • Communities • Economies and money • Camera POV and LOD drives gameplay • Collaborative and dynamic intelligences

  43. AI could be a “killer app” feature of next gen • Characters: • Awareness • Memory • Complex motives, simple commands • 100x RAM allocation • Must be co-developed with animators! • Game AI must be acted out and seen • Expressions & gestures

  44. The Madden Test (of game AI) • 1985: “That’s not football!” • 1990: “I’d fire the coach!” • 1995: “What are those guys doing?” • 2000: “Rookie, you’re cut!” • 2005: “That’s the way I designed it!”

  45. What EA learned from John Madden • The Oakland Raiders playbook • Matchup strategy • 5 zones of field-position • One Knee Equals Two Feet • Player ratings • All-Madden team

  46. Madden Football Genesis23%

  47. Madden 97 Playstation54%

  48. Madden 2001 Playstation 263%

  49. Madden 2005 PS283%

  50. AI is not critical…yet • AI cited for 6/20 top PS2 games. • Metal Gear, NFL x 4, Soccer • AI cited for 3/10 top PC games. • Half Life x 2, Civilization