Transcript feature marketing- the new skill
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
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 – 3 assignments Final grade 4 Final grade 5 Details of the paper and the homework assignments will 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?”
“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
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
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.
Player masters everything in the available patterns.
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.
– 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.
– 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 forward.
– Unfortunately,
problems
.
– A car starts moving sideways on a road instead of – We no longer have a rapid response.
conscious
likely to screw it up.
thought is very
inefficient
.
– If you have to think about what you are doing, you are
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.
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 Intelligence
s
Some basic types of intelligence that entertainment can
appeal to:
– 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
between is to strike a balance 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 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
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
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
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 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
SHRDLU
1 5
Perceived Complexity
5
Actual Complexity
1
-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
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 Peer AI Sub AI Experience - Information Flow - Pacing - Simple Player Model Agents - Behavior - Opponents/Avatars - Complex Player Model Simulation - Physics - Tactile - Intuitive Player Model
Meta Peer Sub SimCity Meta Peer Sub Meta Peer Sub The Sims Meta Peer Sub Meta Peer 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
Player Story Adaptive Mapping Computer Understanding 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
and industrial interests.
keep pace with academic 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.
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
easier for military IE projects to have significant AI components.
with AI research has made it 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: