AI in Digital Entertainment

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

2. Who Cares? • To start with, I hope that you care just because you think this stuff is fun. • But wait, there’s more !!!

3. Who Cares? Submitted Jul 19, 2006: Senior AI Programmer LucasArts Major Responsibilities: The Senior AI Programmer will be responsible for designing an automated system to control the behaviors, short and long term goals, and reasoning of AI This person will be tasked with implementing path finding algorithms and solutions The person in this role will create data driven, customizable, flexible, and robust code, systems, and algorithms Provide technical expertise to develop games or technologies in support of games. … Assist in the sharing of ideas and exploration of new practices to continually improve the quality of software development for the Company …

4. Course Literature • Variety of web sites and electronically available papers. • Join American Association for Artificial Intellilgence (www.aaai.org) • Online access to numerous conference proceedings including “Artificial Intelligence and Interactive Digital Entertainment” proceedings. • One of the best sources of materials on all aspects of AI. • International Student membership - $75 (about 550SEK) • A bargain at twice the price!!

5. 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. • Details of the paper and the homework assignments will (soon) be found on the course web site.

6. Administrivia • Course is graded on 6 degree scale: A-F. • Each homework is worth 150 points. • The final paper is worth 250 points. • Final grades will be assigned as follows: • A: >= 600 points • B: 500 – 600 points • C: 400 – 500 points • D: 300 – 400 points • E: 200 – 300 points • F: < 200 points.

7. “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?”

8. 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

9. Example 1 – Subjective Avatars • Example of work in Interactive Drama. • Goal is to create a story-like experience in which the focus is on interactions with characters, not on solving puzzles. • Offers an opportunity for a user to experience the world from a new viewpoint. • The hope is to combine the empathic understanding of a character achieved by books and movies with the intensity of a first-person interaction.

10. Example 1 – Subjective Avatar • Allow the user to step into the shoes of a character, experiencing a story from this new perspective. • Allow the user to gain an empathic understanding of a character by being this character. • Allow the user to gain insight into the character s/he is playing when s/he is controlling this character’s actions. • If s/he were to immediately begin acting out of character, s/he will derail the story, effectively preventing any insight.

11. Example 2 – Office Plant • Walk into a typical, high tech office environment, and, among the snaking network wires, glowing monitors, and clicking keyboards, you are likely to see a plant. • The silent presence of the plant fills an emotional niche. • Unfortunately, this plant is often dying; it is not adapted to the fluorescent lighting, lack of water, and climate controlled air of the office. • Office Plant is a technological object, adapted to the office ecology, which fills the same social and emotional niche as a plant.

12. Example 2 – Office Plant • Office Plant is a cross between a companion agent and an art object that someone would keep in their office. • It shares a focus on long-term engagement with virtual pets such as Dogz and Catz • However, virtual pets are intended for circumscribed, high-intensity interaction. • In contrast, Office Plant is always on, providing a background ambient commentary on the day’s activity.

13. Example 3 – Terminal Time • A machine that constructs ideologically-biased documentary histories in response to audience feedback. • A cinematic experience, designed for projection on a large screen in a movie theater setting. • At the beginning of the show, and at several points during the show, the audience responds to multiple choice questions reminiscent of marketing polls. E.g., • Which of these phrases do you feel best represents you: • A. Life was better in the time of my grandparents. • B. Life is good and keeps getting better every day.

14. Example 3 – Terminal Time • The audience selects answers to these questions via an applause meter – the answer generating the most applause wins. • The answers to these questions allow the computer program to create historical narratives that attempt to mirror and often exaggerate the audience’s biases and desires. • By exaggerating the ideological position implied in the audience’s answers, Terminal Time produces not the history that they want, but the history that they deserve.

15. Example 4 – Fable 2 Dog • Peter Molyneuax’s laws of NPC Design: • NPCs (aka AIs) in games generally should be there for your entertainment. An NPC should: • Not aggravate the player. • Focus around the player. • Look after itself.

16. Example 4 – Fable 2 Dog • The dog does this by • getting out of your way but still running around you • anticipating your movement and acting as a guide • not barking too much… • Built using a BDI (Belief, Desire, Intention) architecture.

17. 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.

18. 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.

19. 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

20. 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.

21. 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!

22. 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.

23. 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 ...

24. 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.

25. 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.

26. 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.

27. 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.

28. 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

29. 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.

30. 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.

31. 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.

32. 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.

33. From Game to Art • For games to reach art, the mechanics must (one point of view) 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

34. 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.

35. What is Artificial Intelligence

36. Can Machines Have Minds?

37. Two Types of Goals

38. AI and Computer Science

39. Examples of AI Research

40. Other AI Research Areas

41. AI is Inherently Multi-Disciplinary

42. Different Strokes for Different AI Folks

43. AI Programming

44. 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

45. 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

46. 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

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

48. 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

49. 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     

50. 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