AI%20in%20Digital%20Entertainment

1
AI in Digital Entertainment

• Instructor Rand Waltzman
• E-mail rand_at_nada.kth.se
• Phone [phone]
• Room 1430, Lindstedtsvägen 3
• 4 point course
• Periods I and II

2
Administrivia

• There is no tenta 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. Its called life. Maybe youve
played it?
4
The problem with people isnt that they work to
undermine games and make them boring. Thats 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!

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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 enough thought.
• 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.

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Integrative Thought

• Part of the brain that does the chunking.
• Cant 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 appeal to
• 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
  cant 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

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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.

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What is Artificial Intelligence
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Can Machines Have Minds?
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Two Types of Goals
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AI and Computer Science
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Examples of AI Research
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Other AI Research Areas
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AI is Inherently Multi-Disciplinary
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Different Strokes for Different AI Folks
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AI Programming
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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
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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

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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
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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 Thats not football!
• 1990 Id fire the coach!
• 1995 What are those guys doing?
• 2000 Rookie, youre cut!
• 2005 Thats the way I designed it!

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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

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Madden Football Genesis23
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Madden 97 Playstation54
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Madden 2001 Playstation 263
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Madden 2005 PS283
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AI is not criticalyet

• 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

51
When AI is applauded

• Appropriate npc behaviors
• Dynamic adjustments
• Satisfying mistakes
• Coordinated attacks/retreats
• Challenging opponent

52
When AI is punished

• Too easy
• No cover
• Too dumb
• Low awareness
• Deer in headlights

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Observation 1
Maximize the ratio of perceived intelligence to
internal complexity.
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-Put a red pyramid on a green block. gtOK -Pick up
a blue block gtOK
SHRDLU - Winograd
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-Men are all alike. gtIN WHAT WAY? -They're
always bugging us about something or other. gtCAN
YOU THINK OF A SPECIFIC EXAMPLE? -Well, my
boyfriend made me come here. gtYOUR BOYFRIEND
MADE YOU COME HERE -He says I'm depressed much
of the time. gtI AM SORRY TO HEAR YOU ARE
DEPRESSED -It's true. I am unhappy gtDO YOU
THINK COMING HERE WILL HELP YOU NOT TO BE UNHAPPY
Eliza - Weizenbaum
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SHRDLU
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5
1
Perceived Complexity
Actual Complexity
1
5
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Observation 2
The player will build an internal model of your
system. If you dont help them build it, theyll
probably build the wrong one.
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Observation 3
The flow of information about a system has a huge
impact on the players perception of its
intelligence.
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Observation 4
From the players point of view there is a fine
line between complex behavior and random
behavior. Visibility of causal chains usually
makes the difference.
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Observation 5
Mimicking human intelligence and maximizing the
intelligence of an artificial system are 2 very
different tasks.
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Observation 6
There are many applications of AI in games that
dont involve Opponents, Avatars or even
human-like intelligence.
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(No Transcript)
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- Information Flow
- Pacing
- Simple Player Model
Peer AI
- Behavior
- Opponents/Avatars
- Complex Player Model
Sub AI
- Physics
- Tactile
- Intuitive Player Model
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Meta
Meta
Meta
Peer
Peer
Peer
Sub
Sub
Sub
The Sims
Spore
SimCity
Meta Peer Sub
Meta Peer Sub
Meta Peer Sub
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Observation 7
Building a system that collects and reflects
natural intelligence might be easier than
replicating that intelligence.
45
67
Observation 8
Building a robust, internal model of the player
can have huge potential value.
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From the players model of the computertothe
computers model of the player
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Computer Understanding
Player Story
Adaptive Mapping
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AI Research IE Practice

• IE has strong interest for systems that think,
  behave and interact like people.
• Autonomous agents as supporting cast roles.
• Virtual Worlds
• NPCs
• Real Worlds
• Companions
• Collaborators
• Opponents
• Good news for AI research community.
• No simple non-AI engineering solution.

71
Some Daunting Challenges

• Significant difference in the rate of development
  in AI and IE.
• Progress in AI is slow slower than ever.
• IE experiencing explosive growth in both academia
  and industry.
• Slow progress of AI will not keep pace with
  academic and industrial interests.
• E.g., autonomous virtual animated characters.
• Graphics researchers have provided animated
  character bodies approaching realism in
  visualization and animation.
• Capacities for autonomous planning, control,
  conversation, and interaction are barely passable
  for most IE applications.

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Industry Cant Wait

• IE has had to rely on fully scripted interactions
  with human players to support complex
  interactions.
• Exception Basic Combat
• One approach
• Have supporting cast members played by real
  humans.
• In many ways, the rise of multiplayer and
  massively multiplayer IE forms has greatly
  reduced industry need for human-level AI.

73
Social Preferences

• Interacting with other humans in a distributed
  online environment might be preferable for many.
• Result is increased interest in research in
  sociology and social psychology.
• Social network analysis.
• Personality profiling.
• Perhaps more important than the fidelity of NPCs.

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Advice to AI Community

• Be happy that some of the pressure is being
  relieved!
• Broaden the scope of your expertise to include
  elements of the social sciences.

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Follow the Money!

• IE Industry probably has no intention of funding
  basic AI research.
• Traditional flow of software content
• Small developers ?
• Filtered through hardware manufacturers ?
• Large publishers.
• None of these has incentive to support individual
  basic research projects.
• Not for industry-research collaboration either.

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Follow the Money!

• Developers probably have most to gain. But ..
• Tight deadlines.
• Slim profit margins.
• Clash with academic models of high risk
  investigation.
• Ideas more likely to cross the divide than code.
• Expect to see increased interest in academic
  prototypes.
• Implies importance of research funding for
  prototypes.
• Where will this funding come from?
• Wait (!!) it is the cavalry to the rescue ...

77
Necessity is the Mother of Invention

• The military has been the most consistent source
  of AI research funding throughout its entire
  history.
• Increasing reliance on automation and information
  technology superiority.
• Steadily increasing interest in IE.
• E.g., computer game technology for military
• Simulations
• Training
• Recruitment
• Existing comfort level with AI research has made
  it easier for military IE projects to have
  significant AI components.
• And the happy news is ..
• the military is heavily into the tradition of the
  research prototype!

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A Couple of Suggestions

• AI should take advantage of the reduced need for
  human-level AI brought about by increased
  interest in multiplayer and massively multiplayer
  systems.
• Use research-grade AI systems in the automation
  of supporting cast member roles that most humans
  would not find entertaining to play.
• Computational linguistics has been a notable
  exception in the slow pace of AI research.
• Fueled by empirical and statistical methods.
• Few IE researchers have capitalized on the
  potential offered by current technology.

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A Final Word

• If anything you have heard today has upset or
  discouraged you in any way, remember The Guides
  most important bit of advice
• Dont Panic!