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AI in Digital Entertainment
Instructor: Rand Waltzman
E-mail: [email protected]
Phone: 790 6882
Room: 1430, Lindstedtsvägen 3
4 point course
Periods I and II
Who Cares?
To start with, I hope that you care just
because you think this stuff is fun.
But wait, there’s more !!!
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
…
Who Cares?
Current Listings of specifically AI programmer
positions from Gamasutra
(www.gamasutra.com)
– Midway Games -- Chicago, IL, United States
– Offset Software -- Newport Beach, California, United
States
– Concrete Games -- Carlsbad, CA, United States
– Petroglyph -- Las Vegas, Nevada, United States
– Mad Doc Software -- Andover, MA, United States
– NaturalMotion -- Various Locations, United States
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!!
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 (soon) be
found on the course web site.
“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?”
New Possibilities
Application of AI techniques offer potential for
new:
– Media
– Design field
– Art form
Different dimensions to consider:
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Cognitive psychology
Computer science
Environmental design
Storytelling
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.
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.
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
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.
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!
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.
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.
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Get up
Stumble to the bathroom
Take a shower
Get dressed
Drive to work.
Easy enough, but ...
Chunking
What if I ask you to describe one of these steps?
Example: Getting dressed.
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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.
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.
3 Levels of Thought
Conscious thought.
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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.
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.
Appeal to Their Intelligences
Some basic types of intelligence that
entertainment can appeal to:
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Linguistic
Logical-Mathematical
Bodily-Kinesthetic
Spatial
Musical
Interpersonal
Intrapersonal
Internally directed
Self motivated
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.
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.
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.
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.
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
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.
What is Artificial Intelligence
Can Machines Have Minds?
Two Types of Goals
AI and Computer Science
Examples of AI Research
Other AI Research Areas
AI is Inherently Multi-Disciplinary
Different Strokes for Different AI
Folks
AI Programming
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
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
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
ACM Computing Classification
I.2.6 Learning
Analogies
Concept learning
Connectionism and neural nets
Induction
Knowledge acquisition
Language acquisition
Parameter learning
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
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
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
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
ACM Computing Classification
•I.2.11 Distributed Artificial Intelligence
•Coherence and coordination
•Intelligent agents
•Languages and structures
•Multiagent systems
Quality bars of the near-future
“Emergent” behaviors
Believable characters
100x physics
Portable avatars, persistent assets
Communities
Economies and money
Camera POV and LOD drives gameplay
Collaborative and dynamic intelligences
AI could be a “killer app” feature of
next gen
Characters:
– Awareness
– Memory
– Complex motives, simple commands
Must be co-developed with animators!
– Game AI must be acted out and seen
– Expressions & gestures
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!”
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
Madden Football Genesis
23%
Madden 97 Playstation
54%
Madden 2001 Playstation 2
63%
Madden 2005 PS2
83%
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
When AI is applauded…
Appropriate non-player characters (npc)
behaviors
– Dynamic adjustments
– Satisfying mistakes
– Coordinated attacks/retreats
Challenging opponent
When AI is punished…
Too easy
– No cover
Too dumb
– Low awareness
– “Deer in headlights”
Observation #1
Maximize the ratio of
perceived intelligence to
internal complexity.
-Put a red pyramid on a green block.
>OK
-Pick up a blue block
>OK
SHRDLU - Winograd
-Men are all alike.
>IN WHAT WAY?
-They're always bugging us about something or other.
>CAN YOU THINK OF A SPECIFIC EXAMPLE?
-Well, my boyfriend made me come here.
>YOUR BOYFRIEND MADE YOU COME HERE
-He says I'm depressed much of the time.
>I AM SORRY TO HEAR YOU ARE DEPRESSED
-It's true. I am unhappy
>DO YOU THINK COMING HERE WILL HELP YOU NOT TO
BE UNHAPPY
Eliza - Weizenbaum
SHRDLU
-Men are all alike.
>IN WHAT WAY?
-They're always bugging us about something or other.
>CAN YOU THINK OF A SPECIFIC EXAMPLE?
-Well, my boyfriend made me come here.
>YOUR BOYFRIEND MADE YOU COME HERE
-He says I'm depressed much of the time.
>I AM SORRY TO HEAR YOU ARE DEPRESSED
-It's true. I am unhappy
>DO YOU THINK COMING HERE WILL HELP YOU NOT
TO BE UNHAPPY
Eliza
5
1
Perceived
Complexity
Actual
Complexity
5
-Men are all alike.
>IN WHAT WAY?
-They're always bugging us about something or other.
>CAN YOU THINK OF A SPECIFIC EXAMPLE?
1
-Well, my boyfriend made me come here.
>YOUR BOYFRIEND MADE YOU COME HERE
-He says I'm depressed much of the time.
>I AM SORRY TO HEAR YOU ARE DEPRESSED
-It's true. I am unhappy
>DO YOU THINK COMING HERE WILL HELP YOU NOT
TO BE UNHAPPY
SHRDLU
Eliza
Observation #2
The player will build an internal
model of your system.
If you don’t help them build it, they’ll
probably build the wrong one.
Observation #3
The flow of information about a
system has a huge impact on the
players perception of it’s intelligence.
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.
Observation #5
Mimicking human intelligence and
maximizing the intelligence of an artificial
system are 2 very different tasks.
Observation #6
There are many applications of AI in
games that don’t involve Opponents,
Avatars or even human-like intelligence.
Meta AI
Peer AI
Sub AI
Meta AI
Experience
Peer AI
Agents
- Information Flow
- Pacing
- Simple Player Model
- Behavior
- Opponents/Avatars
- Complex Player Model
Sub AI
Simulation
- Physics
- Tactile
- Intuitive Player Model
Meta
Meta
Meta
Peer
Peer
Peer
Sub
Sub
SimCity
Meta
Peer
Sub
The Sims
Meta
Peer
Sub
Sub
Spore
Meta
Peer
Sub
Observation #7
Building a system that collects and
reflects natural intelligence might
be easier than replicating that
intelligence.
45
Observation #8
Building a robust, internal model
of the player can have huge
potential value.
From the player’s model of the computer…to…the computer’s model of the player
Computer
Understanding
Player Story
Adaptive
Mapping
Comedy
Romance
Horror
Mystery
Action
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.
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.
Industry Can’t 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.
– But in other ways it has increased the need.
E.g., how to help orient and retain new participants in a
confusing virtual world.
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.
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.
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.
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 ...
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!
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.
A Final Word
If anything you have heard today has
upset or discouraged you in any way,
remember The Guide’s most important bit
of advice:
Don’t Panic!