Augmenting Human Innovation
with Social Cognition
Ashwin Ram
Cognitive Computing Lab
Collaborators
RSs / Postdocs
Hua Ai
Santi Ontañón
Marco Antonio
Pedro Pablo
Juan Santamaría
Faculty
Mark Riedl
Charles Isbell
Michael Mateas
Startup
Preetha Ram
Chris Sprague
Antonio Salazar
Sid Gupta
Jon Birdsong
PhD students
Manish Mehta
Saurav Sahay
Steve Urban
Toni Barella
Alex Zook
Denis Aleshin
Brian Sherwell
David Llanso
Iulian Radu
Neha Sugandh
Christina Strong
Peng Zang
MS students
Sanjeet Hajarnis
Christina Leber
Ken Hartsook
Hayley Borck
Kane Bonnette
Anna-Marie Mansour
Jai Rad
Rushabh Shah
Kinshuk Mishra
Manu Sharma
Dev Priya
Undergrads
Paritosh Mohan
Gabriel Cebrian
Sam Kim
Katie Long
Tom Amundsen
Eric Johnson
Alistair Jones
Christina Lacey
Andrew Trusty
Funding
DARPA NSF
ONR ARL
AFRL AFOSR
ARDA (IARPA)
Disney SAIC
Yamaha GM
Lockheed GE
DEC
GRA NSF
NIH
My Background
Ø BTech EE IIT Delhi 1982 (Valedictorian)
Ø MS CS Illinois 1984
Ø PhD AI/CogSci Yale 1989
Ø Professor CS Georgia Tech 1989–99
Ø Professor CS/HCC Georgia Tech 2003–
Ø Founder Enkia 1999 (acquired)
Ø Founder Ardext 2000
Ø Founder OpenStudy 2007
Cognitive Computing
Cognitive
Systems
Cognitive
Science
Scholarship
Discovery
(Research)
Integration
Education Application
Boyer
Pasteur’s Quadrant
6
Application Areas
7
Monitor power plants
Control robots
Assist
biomedical
researchers
…and more
Play interactive games
Help students learn
Help information analysts
ARDA (IARPA), DEC
ONR, NSF, NIH, GRA
DARPA, NSF Yamaha, ARL
GE
Augmented Social Cognition…
8
… is creating Disruption
… in Innovation
Human Innovation
9
Who?
Where?
Why?
10
Example: Games ~$11B market
11
“Games”
•  Games are “entering a new era, where technology and
creativity will fuse to produce some of the most stunning
entertainment of the 21st century.” (Doug Lowenstein)
•  “Games are widely used as educational tools, not just for
pilots, soldiers and surgeons, but also in schools and
businesses.” (The Economist)
•  “Serious games” focus on “management and leadership
challenges facing the public sector in education, training,
health, and public policy.” (www.seriousgames.org)
•  “Humane games” include “interactive tools for medical
training, educational games, and games that reflect social
consciousness or advocate for a cause.” (Scott Leutenneger)
Social Gaming
12
~$2B market
$400M acquisition
60 90 200M users
12M users
$100M market cap
$1B virtual goods economy
~$1.60 ARPU
+ 20M + 20M + …
Consumers Want To Innovate
13
$400M acquisition
60 90 200M users
12M users
$100M market cap
$1B virtual goods economy
~$1.60 ARPU
Consumers
Participants
Creators
+ 20M + 20M + …
Users as Creators
User Created Games
Worldwide market
• $80 million in 2006
• $1.1 billion in 2007
• ~$13 billion by 2011
KongregateAddicting Games
More than $7.3 billion
by 2013
Juniper	
  Research	
  
User Created Content
Habbo Hotel IMVU
IMVU: 1.6 million
user generated
virtual items
Easy as Pie
Teaching game
programming
to 3rd graders:
Scratch
Human Innovation Is Everywhere
16
Human Innovation Is Everywhere
17
Human Innovation Is Everywhere
18
How do we…
understand innovation
support innovation
foster innovation?
Augmented Social Cognition
The Long Tail of Innovation
19
Key question:
How to support, nurture, enhance
innovation for all people?
Why Innovation Matters
20
Where Have You Gone, Bell Labs?
How basic research can repair the
broken U.S. business model
“legendary institutions such as Bell Labs, RCA Labs,
Xerox PARC, IBM, DARPA, NASA”
Innovation: Economic Driver
21
Research turns money into ideas.
Innovation turns ideas into money.
Discovery
(Research)
Integration
Education Application
Bo
yer
Innovation: Economic Growth
22
Innovation: Competitive Advantage
23
Innovation: Job Creation
24
without startups, net job creation for the US economy
would be negative in all but a handful of years
Innovation Value Chain
Creativity
Learning
Problem Solving
25
Challenge Problems
Healthcare
Energy
Education
Entertainment
Help people solve problems
Creativity
Learning
Problem Solving
27
Healthcare
with Augmented Social Cognition
How do humans solve problems?
Healthcare Information
Copyright
(c) 2009
Cobot.
Confident
ial &
Internet is the leading source
of health and wellness information.
Health 1.0 à Health 2.0
Copyright
(c) 2009
Cobot.
Confident
ial &
Healthcare Info Access without Search
Cobot: Social Healthcare Agent
Help people solve problems
Creativity
Learning
Problem Solving
33
Energy
with Augmented Social Cognition
Complex Systems M&D
Business Context
Gas Turbine’s control system protects the
unit from possible damage by executing a
sudden shutdown (trip) logic.
Alarm log files and real-time data are
transferred to Atlanta.
GE M&D Center troubleshoots each trip
and calls customer with recommendations.
On-Site Monitor
Critical Success Factors
ü  Decision Support
§  Keep human in the loop
§  Maintain established business workflow
ü  Accuracy and Timeliness
§  Very high accuracy even with imperfect data
§  Provide top recommendation and alternatives
§  Explain recommendation and estimate confidence
ü  Maintainability
§  Extend coverage to new sensors, failures, equipment
§  Provide automated optimization and maintenance tools
ü  Real deployable system (not a prototype)
Case-Based Reasoning captures
community knowledge
Solution Architecture
Copyright © 2006 Enkia Corporation
All Rights Reserved
Case-Based Reasoning (CBR)
Case Base Maintenance (CBM)
Enkia SentinelTM
Results
§  Tested in production environment in 2004-05
§  System output integrated into troubleshooting
workflow as recommendation
§  Additional failure modes added by GE without
any modification to system
§  >90% accuracy (exact metrics proprietary)
Help people learn
Creativity
Learning
Problem Solving
40
with Augmented Social Cognition
Help people learn
Creativity
Learning
Problem Solving
41
Education
with Augmented Social Cognition
The Long Tail
42
Experts
Everyone
The Long Tail of Education
43
Traditional
More than one-third of the world’s
population is under 20. There are over
30 million people today qualified to enter
a university who have no place to go.
During the next decade, this 30 million
will grow to 100 million. To meet this
staggering demand, a major university
needs to be created each week.
— Sir John Daniel (1996)
— John Seely Brown (2008)
The Long Tail of Education
44
Traditional
Open Education
MIT OCW: 9m users/yr
iTunes U: 300m downloads
Khan: 30k videos/day
Great video and talented
presenters. My only complaint:
I’d like to interact with others
who are viewing the resources.
Creating a one-way flow of
information significantly misses
the point of interacting online.
— George Siemens ( 2007)
Online content is only half the answer
46
US 2009
self paced
learning
market
= $16.7bn
New $3bn
market
4,000 free courses
30 million learners
study help, tutoring,
certification, recruiting
Availability
Growing to 100 million users
$3bn market opportunity
30 million users of
free online courses
Size Of Problem
Home
Schoolers
OpenStudy: Social Learning Network
Traditional
Self Learners
Community
Colleges
OpenStudy is…
48
“a social platform for learners
who want to help each other study”
“you’re no longer alone—you have the world’s
biggest classroom to turn to, any time, anywhere”
“a global study group”
“global element is important…users
will almost always find someone
online in the study groups”
one of ten most innovative
companies in education
49
OpenStudy.com
Massively Multiplayer
Online Learning
Gates/Hewlett
NSF NIH
GRA GT Emory
51
OpenStudy Demo
OpenStudy Secret Sauce
52
User Experience Design
Really Real-Time
Collaboration
AI
Recommendation
Engine
Social Media
Analytics
Social Capital
Engine
Help people create
Creativity
Learning
Problem Solving
53
with Augmented Social Cognition
Help people create
Creativity
Learning
Problem Solving
54
Entertainment
55
Games
User Created Games
KongregateAddicting Games
User Created Content
Habbo Hotel IMVU
Users as Creators
Easy as Pie
But…	
  
Users	
  can	
  create	
  physical	
  ar/facts	
  
Cannot	
  create	
  	
  
interes/ng	
  personali/es	
  
Not a
Robot
Users	
  can	
  create	
  simple	
  games	
  
But…	
  
Cannot	
  create	
  	
  
intelligent	
  behaviors	
  
Towards “AI 2.0”
• Problem:
– User-created content is everywhere: photos,
videos, news, blogs, virtual goods, avatars,
games…
…but not AI
• Vision:
– Allow end-users to build AIs
– Provide a new kind of social gaming experience
based around creativity
59
What is Game AI?
•  AI powers the game characters
–  “Believable agents” with complex behaviors
–  Focused on NPC’s own goals
–  Embedded in the game
60
AI for NPCs
Wargus!
Tag

(Unreal)!
61
What is Game AI?
•  AI powers the game characters
–  “Believable agents” with complex behaviors
–  Focused on NPC’s own goals
–  Embedded in the game
•  AI powers the game director
–  “Drama manager” (DM) with global perspective
–  Focused on author’s rhetorical and affective goals
–  Watches over the game-player interaction
–  Enhances player experience
AI for DM
62
Player!
Model!
DM!
actions!
Player!
Story!
state!
History!
Physical!
state!
Game State!
Game !
Engine!
Player!
Modeling!
Drama
Manager!
Player!
Trace!
Guide the player to a more enjoyable experience"
GOAL
Anchorhead

(1998)!
That silver locket
looks curious!
Why is Game AI hard?
•  Huge Decision Space
(thousands of possible
states and actions)
•  Cognitive Modeling
(goals, strategies, plans,
tactics, behaviors,
personalities, teams)
•  Non-Deterministic and
Real-Time
•  Classical approaches
don’t work directly
Aha et al (ICCBR-05)
64
Games are AI-complete
•  “Games require players to construct hypothesis, solve
problems, develop strategies, learn the rules of a new world
through trial and error.”
•  “Gamers must be able to juggle several different tasks,
evaluate risks, and make quick decisions.” (The Economist)
Well then, so must Game AI !
65
Game AI is hard
Problem: How will end-users create AIs?
…even for experts
66
User-Generated AI…
build a “mind” without programming
Users create behaviors
visually
System fixes “bugs” during
behavior execution
User “programs” behaviors
by demonstration
67
User-Generated AI…
build a “mind” without programming
Behavior authoring
Behavior adaptation
Behavior demonstration
68
User-Generated AI…
build a “mind” without programming
Behavior authoring
Behavior adaptation
Behavior demonstration
1) Behavior Authoring…
69
Second Mind Updates…
Flirting with girl at bar
Teach your avatar how will you flirt …
Posted By: 2MGuru ***
More Join
Get coffee spilled on you
Show how will you react if coffee …
Posted By: 2MGuru ***
Fool your boss
Imagine how many ways you can …
Posted By: 2MGuru ***
More Join
More Join
2MGuru
Login to
Virtual World
1
Flirting with girl
at bar
Teach your avatar how will you
flirt …
Posted By: 2MGuru ***
Fool your boss
Imagine how many ways you can
…
Posted By: 2MGuru ***
Acti
vate
I
n
v
i
t
e
Act
iva
te
In
vi
te
SMPlayer
2MGuru
Flirt
with Girl
Choose a
Scene
2
SMPlayer
2MGuru
Flirt with Girl at
bar
Hey!! Wanna play. I am looking
for a partner.
Sure! Lets find a suitable
location…
SMPlayer
Christina
Flirt with Girl at
bar
Choose a
Character
3
Hi
H
ow
ar
e
thi
ng
s
go
in
g
Role play your
sceneGreat!
How
about
you …
Hi.
How
are
you
doing
…
Hi How are
things
going I am
doing
great
Not at all!
Sure…
Do you mind
if I join you? …
Play your
Scene
5 Save in
Shared Library
6
Behaviors
Personalities
Stories
Greet Flirt
00
:0
0	
  
0
0
:
0
0	
  
Impress
My	
  Scene:	
  
Impress	
  a	
  Date	
  
Hi! How are
you?
walks closer
I
m
pr
es
s
b
os
s
Imp
res
s
More	
  op/ons…	
  
Imp
res
s
girl
MY SCENE:
Flirt with a girl
00:00
Author your
Behaviors
4
Second Mind
70
other
minds
Goals	
  
Ac8ons	
  
Goal: Greet a customer	
  
00:0
0	
  
00:
00	
  
Sensory
info
Your goal
Other’s
goal
Question
How	
  
much	
  is	
  
the	
  
vase	
  for	
  
Answer Pleased
This	
  
vase	
  is	
  
…………	
  
Sensory?
Second Mind
71
other
minds
Goals	
  
Ac8ons	
  
Goal: Greet a customer	
  
00:0
0	
  
00:
00	
  
Sensory
info
Your goal
Other’s
goal
Question
How	
  
much	
  is	
  
the	
  
vase	
  for	
  
Answer Pleased
This	
  
vase	
  is	
  
…………	
  
Sensory?
72
User-Generated AI…
build a “mind” without programming
Behavior editing
Behavior adaptation
Behavior learning
73
2) Behavior Adaptation
Hand-authored	

character is not	

quite right	

Tag

(Unreal)!
How to detect
break in user
experience?
Meta Reasoning (Reflection)
Intelligent
NPC
Goals
Game
Environment
Behavior
Library
Reasoning
Trace
Abstracted
Trace
Trace
Evaluation
Mod-Ops
Pattern
Matching
Failure
Patterns
75
AIs that repair themselves
76
User-Generated AI…
build a “mind” without programming
Behavior editing
Behavior debugging
Behavior demonstration
3) Behavior Demonstration
Make it even easier:	

	

• User demonstrates how
the AI should behave
	

• Systems builds the AI
automatically	

Key technology:	

• Real-Time CBR
Darmok 2
•  Real-Time Case-Based Planning  Learning system
•  Designed to play RTS games,
but domain independent
•  Automatically Learns Cases from Demonstration
•  Available on SourceForge
Darmok 2: Plan Representation
•  Plans represented as Hierarchical Petri Nets:
–  Sequential
–  Parallel
–  Conditionals
–  Loops
–  Primitive Actions
–  Subgoals (hierarchy)
•  Plans learned automatically from observations
•  Petri Nets are very expressive
–  Darmok 2 is limited to what the learning module can
produce (e.g. currently no loops)
Darmok 2: Case Representation
S0: 1
S1: 0
Gold400
ExistsPath(E5,(17,18))
S3: 0
S4: 0
NewUnitBy(U4)
!Exists(E4)
S2: 0
Status(E5)==“harvest”
0
Timeout(500)0
!Exists(E5)0
Train
(E4,”peasant”)
Harvest(E5,
(17,18))
Timeout(500)0
gamestate
entity id=“E14“ type = “Player”
gold1200/gold
wood1000/wood
ownerplayer1/owner
/entity
entity id=“E15“ type = “Player”
gold1200/gold
wood1000/wood
ownerplayer2/owner
/entity
entity id=“E4“ type = “Townhall”
x6/x
y0/y
ownerplayer1/owner
hitpoints2400/hitpoints
/entity
…
/gamestate
Wood300
GOAL:
STATE:
Snippet 1:
1.0
OUTCOME:
Episode 1:
Darmok 2: Architecture
Adversarial
Planner
Case
Base
Plan
Execution
Case
Retrieval
Case
Learning
Plan
Adaptation
Real-Time
Minimax
Plan
Simulation
Plan
Game State
Opponents
Model
Opponents
Model
Learning
Demonstrations
GameActions
Model
Planning Learning Data Handcrafted Data
Learning by Demonstration
1
2
3 4
5 6
7 9
8
10 11
G2
9 8
10 11
Demonstration G1 G2
t1,GS1,A1 0 0
t2,GS2,A2 0 0
t3,GS3,A3 0 0
t4,GS4,A4 0 0
t5,GS5,A5 0 0
t6,GS6,A6 0 0
t7,GS7,A7 0 1
t8,GS8,A8 0 1
t9,GS9,A9 0 1
t10,GS10,A10 0 1
t11,GS11,A11 0 1
t12,GS12,- 1 1
83
Reasoner uses 
case-based function
approximator	

	

	

	

	

	

	

Learner modifies	

• Case library	

• Input parameters	

• Action policies	

	

Integrates CBR with
SVM and TD-learning	

	

	

Real-Time Reactive Planning
ΔQi = α ( rt + 1 + γ Qt + 1 + Qt ) ei ∀Ci∈memory
SM(E,C, p) =
wj
j=1
J
!
(Einput j
(i)  Cinput j
( p  i))2
p +1i=0
min( p,lE )
!
+ wk
(Eoutput k
(i)  Coutputk
( p  i))2
p +1i 0
min( p,lE )
!k =1
K
!
84
Reasoner splices
snippets of previous
plans to solve new
problems 	

	

Learner adapts and
remembers new
solutions for future
use	

	

Integrates CBR with
HTN planning	

	

Multi-Plan CBR
Ram  Francis (1996)
Real-Time Minimax Planner
•  Adversarial
•  Plans adapted in real-time
•  Interleaved planning and
execution
!#$%'
()%)*'
(+'
(,'
(-'
(+' (+'
(.'
(/'
(0'
1%2'3+'45+65,63,65-7'
8!9'9:;'
1%2'3+'45+65,63,'45.65/765-7'
8!9'9:;'
1%2'3+'45+65,63,65-7'
85='9:;'
1%2'3+'45+65,63,65-7'
1%2'3+'45+65,63,65-7'
1%2'3+'45+65,63,'4506565?765-7'
(#@A%)*' (#@A%)*'
(#@A%)*'
(#@A%)*'(#@A%)*'
(#@A%)*'
1%'BCD'3,2'5.65/' 1%'BCD'3,2'506565?'
Test Domains
• Simplified Warcraft 2
• Important factors:
• Terrain
• Resources
• Buildings
• Units
S2
• Defense Game
• Important factors:
• Build order
• Spatial formation
Towers
• Fast Paced Action
• Important factors:
• Reflexes
• Navigation
BattleCity
Experimental Results
Each point is average of 50 games over 5 different maps
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
1 2 3 4 5 6 7 8 9 10
BC
Towers
S2
Linear (BC)
Linear (Towers)
Linear (S2)
Towers
S2
Battle
City
Make ME Play ME
•  Social Gaming website
powered by Darmok 2
•  Players create MEs which
can play games
•  Players can challenge MEs
created by other players
•  First product to allow
end-users to create
sophisticated AIs
ME = Mind Engine
TM
TM
MakeMEPlayME.com
90
sourceforge.net/projects/darmok2
makemeplayme.com
secondmind.org
Augmented Social Cognition Innovation
Creativity
Learning
Problem Solving
91
Healthcare
Energy
Education
Entertainment
Make your own Mind Engines
Funded by NSF
Make your own Second Mind
93
other
minds
Goals	
  
Ac8ons	
  
Goal: Greet a customer	
  
00:0
0	
  
00:
00	
  
Sensory
info
Your goal
Other’s
goal
Question
How	
  
much	
  is	
  
the	
  
vase	
  for	
  
Answer Pleased
This	
  
vase	
  is	
  
…………	
  
Sensory? Second Life
Funded by Disney
Make your own Interactive Stories
94
Lorem ipsum dolor sit amet.
Lorem!
Lorem ipsum dolor sit amet,
consectetur adipiscing elit.
Proin consequat imperdiet
tincidunt. Aliquam blandit,.
Lorem ipsum dolor sit amet. Lorem ipsum
dolor sit amet.Lorem ipsum dolor sit amet, consectetur
adipiscing elit. Proin consequat imperdiet
tincidunt. Aliquam blandit,
Lorem ipsum dolor sit amet, consectetur
adipiscing elit. Proin consequat imperdiet
tincidunt.
Don’t tell me, SHOW ME
With Mark Riedl / Funded by DARPA
Vision
95
Creativity
Learning
Problem Solving
Up…Right…and Real
96
Contact
ashwinram@me.com
@ashwinram
www.cc.gatech.edu/~ashwin
linkedin.com/in/ashwinram

Augmented Social Innovation

  • 1.
    Augmenting Human Innovation withSocial Cognition Ashwin Ram Cognitive Computing Lab
  • 2.
    Collaborators RSs / Postdocs HuaAi Santi Ontañón Marco Antonio Pedro Pablo Juan Santamaría Faculty Mark Riedl Charles Isbell Michael Mateas Startup Preetha Ram Chris Sprague Antonio Salazar Sid Gupta Jon Birdsong PhD students Manish Mehta Saurav Sahay Steve Urban Toni Barella Alex Zook Denis Aleshin Brian Sherwell David Llanso Iulian Radu Neha Sugandh Christina Strong Peng Zang MS students Sanjeet Hajarnis Christina Leber Ken Hartsook Hayley Borck Kane Bonnette Anna-Marie Mansour Jai Rad Rushabh Shah Kinshuk Mishra Manu Sharma Dev Priya Undergrads Paritosh Mohan Gabriel Cebrian Sam Kim Katie Long Tom Amundsen Eric Johnson Alistair Jones Christina Lacey Andrew Trusty Funding DARPA NSF ONR ARL AFRL AFOSR ARDA (IARPA) Disney SAIC Yamaha GM Lockheed GE DEC GRA NSF NIH
  • 3.
    My Background Ø BTech EEIIT Delhi 1982 (Valedictorian) Ø MS CS Illinois 1984 Ø PhD AI/CogSci Yale 1989 Ø Professor CS Georgia Tech 1989–99 Ø Professor CS/HCC Georgia Tech 2003– Ø Founder Enkia 1999 (acquired) Ø Founder Ardext 2000 Ø Founder OpenStudy 2007
  • 4.
  • 5.
  • 6.
  • 7.
    Application Areas 7 Monitor powerplants Control robots Assist biomedical researchers …and more Play interactive games Help students learn Help information analysts ARDA (IARPA), DEC ONR, NSF, NIH, GRA DARPA, NSF Yamaha, ARL GE
  • 8.
    Augmented Social Cognition… 8 …is creating Disruption … in Innovation
  • 9.
  • 10.
  • 11.
    11 “Games” •  Games are“entering a new era, where technology and creativity will fuse to produce some of the most stunning entertainment of the 21st century.” (Doug Lowenstein) •  “Games are widely used as educational tools, not just for pilots, soldiers and surgeons, but also in schools and businesses.” (The Economist) •  “Serious games” focus on “management and leadership challenges facing the public sector in education, training, health, and public policy.” (www.seriousgames.org) •  “Humane games” include “interactive tools for medical training, educational games, and games that reflect social consciousness or advocate for a cause.” (Scott Leutenneger)
  • 12.
    Social Gaming 12 ~$2B market $400Macquisition 60 90 200M users 12M users $100M market cap $1B virtual goods economy ~$1.60 ARPU + 20M + 20M + …
  • 13.
    Consumers Want ToInnovate 13 $400M acquisition 60 90 200M users 12M users $100M market cap $1B virtual goods economy ~$1.60 ARPU Consumers Participants Creators + 20M + 20M + …
  • 14.
    Users as Creators UserCreated Games Worldwide market • $80 million in 2006 • $1.1 billion in 2007 • ~$13 billion by 2011 KongregateAddicting Games More than $7.3 billion by 2013 Juniper  Research  
  • 15.
    User Created Content HabboHotel IMVU IMVU: 1.6 million user generated virtual items Easy as Pie Teaching game programming to 3rd graders: Scratch
  • 16.
    Human Innovation IsEverywhere 16
  • 17.
    Human Innovation IsEverywhere 17
  • 18.
    Human Innovation IsEverywhere 18 How do we… understand innovation support innovation foster innovation? Augmented Social Cognition
  • 19.
    The Long Tailof Innovation 19 Key question: How to support, nurture, enhance innovation for all people?
  • 20.
    Why Innovation Matters 20 WhereHave You Gone, Bell Labs? How basic research can repair the broken U.S. business model “legendary institutions such as Bell Labs, RCA Labs, Xerox PARC, IBM, DARPA, NASA”
  • 21.
    Innovation: Economic Driver 21 Researchturns money into ideas. Innovation turns ideas into money. Discovery (Research) Integration Education Application Bo yer
  • 22.
  • 23.
  • 24.
    Innovation: Job Creation 24 withoutstartups, net job creation for the US economy would be negative in all but a handful of years
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  • 27.
    Help people solveproblems Creativity Learning Problem Solving 27 Healthcare with Augmented Social Cognition
  • 28.
    How do humanssolve problems?
  • 29.
    Healthcare Information Copyright (c) 2009 Cobot. Confident ial& Internet is the leading source of health and wellness information.
  • 30.
    Health 1.0 àHealth 2.0 Copyright (c) 2009 Cobot. Confident ial &
  • 31.
    Healthcare Info Accesswithout Search
  • 32.
  • 33.
    Help people solveproblems Creativity Learning Problem Solving 33 Energy with Augmented Social Cognition
  • 34.
  • 35.
    Business Context Gas Turbine’scontrol system protects the unit from possible damage by executing a sudden shutdown (trip) logic. Alarm log files and real-time data are transferred to Atlanta. GE M&D Center troubleshoots each trip and calls customer with recommendations. On-Site Monitor
  • 36.
    Critical Success Factors ü Decision Support §  Keep human in the loop §  Maintain established business workflow ü  Accuracy and Timeliness §  Very high accuracy even with imperfect data §  Provide top recommendation and alternatives §  Explain recommendation and estimate confidence ü  Maintainability §  Extend coverage to new sensors, failures, equipment §  Provide automated optimization and maintenance tools ü  Real deployable system (not a prototype)
  • 37.
  • 38.
    Solution Architecture Copyright ©2006 Enkia Corporation All Rights Reserved Case-Based Reasoning (CBR) Case Base Maintenance (CBM) Enkia SentinelTM
  • 39.
    Results §  Tested inproduction environment in 2004-05 §  System output integrated into troubleshooting workflow as recommendation §  Additional failure modes added by GE without any modification to system §  >90% accuracy (exact metrics proprietary)
  • 40.
    Help people learn Creativity Learning ProblemSolving 40 with Augmented Social Cognition
  • 41.
    Help people learn Creativity Learning ProblemSolving 41 Education with Augmented Social Cognition
  • 42.
  • 43.
    The Long Tailof Education 43 Traditional More than one-third of the world’s population is under 20. There are over 30 million people today qualified to enter a university who have no place to go. During the next decade, this 30 million will grow to 100 million. To meet this staggering demand, a major university needs to be created each week. — Sir John Daniel (1996) — John Seely Brown (2008)
  • 44.
    The Long Tailof Education 44 Traditional Open Education MIT OCW: 9m users/yr iTunes U: 300m downloads Khan: 30k videos/day
  • 45.
    Great video andtalented presenters. My only complaint: I’d like to interact with others who are viewing the resources. Creating a one-way flow of information significantly misses the point of interacting online. — George Siemens ( 2007) Online content is only half the answer
  • 46.
    46 US 2009 self paced learning market =$16.7bn New $3bn market 4,000 free courses 30 million learners study help, tutoring, certification, recruiting Availability Growing to 100 million users $3bn market opportunity 30 million users of free online courses Size Of Problem
  • 47.
    Home Schoolers OpenStudy: Social LearningNetwork Traditional Self Learners Community Colleges
  • 48.
    OpenStudy is… 48 “a socialplatform for learners who want to help each other study” “you’re no longer alone—you have the world’s biggest classroom to turn to, any time, anywhere” “a global study group” “global element is important…users will almost always find someone online in the study groups” one of ten most innovative companies in education
  • 49.
  • 50.
  • 51.
  • 52.
    OpenStudy Secret Sauce 52 UserExperience Design Really Real-Time Collaboration AI Recommendation Engine Social Media Analytics Social Capital Engine
  • 53.
    Help people create Creativity Learning ProblemSolving 53 with Augmented Social Cognition
  • 54.
  • 55.
  • 56.
    User Created Games KongregateAddictingGames User Created Content Habbo Hotel IMVU Users as Creators Easy as Pie
  • 57.
    But…   Users  can  create  physical  ar/facts   Cannot  create     interes/ng  personali/es   Not a Robot Users  can  create  simple  games   But…   Cannot  create     intelligent  behaviors  
  • 58.
    Towards “AI 2.0” • Problem: – User-createdcontent is everywhere: photos, videos, news, blogs, virtual goods, avatars, games… …but not AI • Vision: – Allow end-users to build AIs – Provide a new kind of social gaming experience based around creativity
  • 59.
    59 What is GameAI? •  AI powers the game characters –  “Believable agents” with complex behaviors –  Focused on NPC’s own goals –  Embedded in the game
  • 60.
  • 61.
    61 What is GameAI? •  AI powers the game characters –  “Believable agents” with complex behaviors –  Focused on NPC’s own goals –  Embedded in the game •  AI powers the game director –  “Drama manager” (DM) with global perspective –  Focused on author’s rhetorical and affective goals –  Watches over the game-player interaction –  Enhances player experience
  • 62.
    AI for DM 62 Player! Model! DM! actions! Player! Story! state! History! Physical! state! GameState! Game ! Engine! Player! Modeling! Drama Manager! Player! Trace! Guide the player to a more enjoyable experience" GOAL Anchorhead
 (1998)! That silver locket looks curious!
  • 63.
    Why is GameAI hard? •  Huge Decision Space (thousands of possible states and actions) •  Cognitive Modeling (goals, strategies, plans, tactics, behaviors, personalities, teams) •  Non-Deterministic and Real-Time •  Classical approaches don’t work directly Aha et al (ICCBR-05)
  • 64.
    64 Games are AI-complete • “Games require players to construct hypothesis, solve problems, develop strategies, learn the rules of a new world through trial and error.” •  “Gamers must be able to juggle several different tasks, evaluate risks, and make quick decisions.” (The Economist) Well then, so must Game AI !
  • 65.
    65 Game AI ishard Problem: How will end-users create AIs? …even for experts
  • 66.
    66 User-Generated AI… build a“mind” without programming Users create behaviors visually System fixes “bugs” during behavior execution User “programs” behaviors by demonstration
  • 67.
    67 User-Generated AI… build a“mind” without programming Behavior authoring Behavior adaptation Behavior demonstration
  • 68.
    68 User-Generated AI… build a“mind” without programming Behavior authoring Behavior adaptation Behavior demonstration
  • 69.
    1) Behavior Authoring… 69 SecondMind Updates… Flirting with girl at bar Teach your avatar how will you flirt … Posted By: 2MGuru *** More Join Get coffee spilled on you Show how will you react if coffee … Posted By: 2MGuru *** Fool your boss Imagine how many ways you can … Posted By: 2MGuru *** More Join More Join 2MGuru Login to Virtual World 1 Flirting with girl at bar Teach your avatar how will you flirt … Posted By: 2MGuru *** Fool your boss Imagine how many ways you can … Posted By: 2MGuru *** Acti vate I n v i t e Act iva te In vi te SMPlayer 2MGuru Flirt with Girl Choose a Scene 2 SMPlayer 2MGuru Flirt with Girl at bar Hey!! Wanna play. I am looking for a partner. Sure! Lets find a suitable location… SMPlayer Christina Flirt with Girl at bar Choose a Character 3 Hi H ow ar e thi ng s go in g Role play your sceneGreat! How about you … Hi. How are you doing … Hi How are things going I am doing great Not at all! Sure… Do you mind if I join you? … Play your Scene 5 Save in Shared Library 6 Behaviors Personalities Stories Greet Flirt 00 :0 0   0 0 : 0 0   Impress My  Scene:   Impress  a  Date   Hi! How are you? walks closer I m pr es s b os s Imp res s More  op/ons…   Imp res s girl MY SCENE: Flirt with a girl 00:00 Author your Behaviors 4
  • 70.
    Second Mind 70 other minds Goals   Ac8ons   Goal: Greet a customer   00:0 0   00: 00   Sensory info Your goal Other’s goal Question How   much  is   the   vase  for   Answer Pleased This   vase  is   …………   Sensory?
  • 71.
    Second Mind 71 other minds Goals   Ac8ons   Goal: Greet a customer   00:0 0   00: 00   Sensory info Your goal Other’s goal Question How   much  is   the   vase  for   Answer Pleased This   vase  is   …………   Sensory?
  • 72.
    72 User-Generated AI… build a“mind” without programming Behavior editing Behavior adaptation Behavior learning
  • 73.
    73 2) Behavior Adaptation Hand-authored characteris not quite right Tag
 (Unreal)! How to detect break in user experience?
  • 74.
  • 75.
  • 76.
    76 User-Generated AI… build a“mind” without programming Behavior editing Behavior debugging Behavior demonstration
  • 77.
    3) Behavior Demonstration Makeit even easier: • User demonstrates how the AI should behave • Systems builds the AI automatically Key technology: • Real-Time CBR
  • 78.
    Darmok 2 •  Real-TimeCase-Based Planning Learning system •  Designed to play RTS games, but domain independent •  Automatically Learns Cases from Demonstration •  Available on SourceForge
  • 79.
    Darmok 2: PlanRepresentation •  Plans represented as Hierarchical Petri Nets: –  Sequential –  Parallel –  Conditionals –  Loops –  Primitive Actions –  Subgoals (hierarchy) •  Plans learned automatically from observations •  Petri Nets are very expressive –  Darmok 2 is limited to what the learning module can produce (e.g. currently no loops)
  • 80.
    Darmok 2: CaseRepresentation S0: 1 S1: 0 Gold400 ExistsPath(E5,(17,18)) S3: 0 S4: 0 NewUnitBy(U4) !Exists(E4) S2: 0 Status(E5)==“harvest” 0 Timeout(500)0 !Exists(E5)0 Train (E4,”peasant”) Harvest(E5, (17,18)) Timeout(500)0 gamestate entity id=“E14“ type = “Player” gold1200/gold wood1000/wood ownerplayer1/owner /entity entity id=“E15“ type = “Player” gold1200/gold wood1000/wood ownerplayer2/owner /entity entity id=“E4“ type = “Townhall” x6/x y0/y ownerplayer1/owner hitpoints2400/hitpoints /entity … /gamestate Wood300 GOAL: STATE: Snippet 1: 1.0 OUTCOME: Episode 1:
  • 81.
    Darmok 2: Architecture Adversarial Planner Case Base Plan Execution Case Retrieval Case Learning Plan Adaptation Real-Time Minimax Plan Simulation Plan GameState Opponents Model Opponents Model Learning Demonstrations GameActions Model Planning Learning Data Handcrafted Data
  • 82.
    Learning by Demonstration 1 2 34 5 6 7 9 8 10 11 G2 9 8 10 11 Demonstration G1 G2 t1,GS1,A1 0 0 t2,GS2,A2 0 0 t3,GS3,A3 0 0 t4,GS4,A4 0 0 t5,GS5,A5 0 0 t6,GS6,A6 0 0 t7,GS7,A7 0 1 t8,GS8,A8 0 1 t9,GS9,A9 0 1 t10,GS10,A10 0 1 t11,GS11,A11 0 1 t12,GS12,- 1 1
  • 83.
    83 Reasoner uses case-basedfunction approximator Learner modifies • Case library • Input parameters • Action policies Integrates CBR with SVM and TD-learning Real-Time Reactive Planning ΔQi = α ( rt + 1 + γ Qt + 1 + Qt ) ei ∀Ci∈memory SM(E,C, p) = wj j=1 J ! (Einput j (i) Cinput j ( p i))2 p +1i=0 min( p,lE ) ! + wk (Eoutput k (i) Coutputk ( p i))2 p +1i 0 min( p,lE ) !k =1 K !
  • 84.
    84 Reasoner splices snippets ofprevious plans to solve new problems Learner adapts and remembers new solutions for future use Integrates CBR with HTN planning Multi-Plan CBR Ram Francis (1996)
  • 85.
    Real-Time Minimax Planner • Adversarial •  Plans adapted in real-time •  Interleaved planning and execution !#$%' ()%)*' (+' (,' (-' (+' (+' (.' (/' (0' 1%2'3+'45+65,63,65-7' 8!9'9:;' 1%2'3+'45+65,63,'45.65/765-7' 8!9'9:;' 1%2'3+'45+65,63,65-7' 85='9:;' 1%2'3+'45+65,63,65-7' 1%2'3+'45+65,63,65-7' 1%2'3+'45+65,63,'4506565?765-7' (#@A%)*' (#@A%)*' (#@A%)*' (#@A%)*'(#@A%)*' (#@A%)*' 1%'BCD'3,2'5.65/' 1%'BCD'3,2'506565?'
  • 86.
    Test Domains • Simplified Warcraft2 • Important factors: • Terrain • Resources • Buildings • Units S2 • Defense Game • Important factors: • Build order • Spatial formation Towers • Fast Paced Action • Important factors: • Reflexes • Navigation BattleCity
  • 87.
    Experimental Results Each pointis average of 50 games over 5 different maps 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% 1 2 3 4 5 6 7 8 9 10 BC Towers S2 Linear (BC) Linear (Towers) Linear (S2) Towers S2 Battle City
  • 88.
    Make ME PlayME •  Social Gaming website powered by Darmok 2 •  Players create MEs which can play games •  Players can challenge MEs created by other players •  First product to allow end-users to create sophisticated AIs ME = Mind Engine TM TM
  • 89.
  • 90.
  • 91.
    Augmented Social CognitionInnovation Creativity Learning Problem Solving 91 Healthcare Energy Education Entertainment
  • 92.
    Make your ownMind Engines Funded by NSF
  • 93.
    Make your ownSecond Mind 93 other minds Goals   Ac8ons   Goal: Greet a customer   00:0 0   00: 00   Sensory info Your goal Other’s goal Question How   much  is   the   vase  for   Answer Pleased This   vase  is   …………   Sensory? Second Life Funded by Disney
  • 94.
    Make your ownInteractive Stories 94 Lorem ipsum dolor sit amet. Lorem! Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin consequat imperdiet tincidunt. Aliquam blandit,. Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet.Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin consequat imperdiet tincidunt. Aliquam blandit, Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin consequat imperdiet tincidunt. Don’t tell me, SHOW ME With Mark Riedl / Funded by DARPA
  • 95.
  • 96.