cs 440 / ece 448 introduction to artificial intelligence spring 2010 instructor: eyal amir grad tas:...
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![Page 1: CS 440 / ECE 448 Introduction to Artificial Intelligence Spring 2010 Instructor: Eyal Amir Grad TAs: Wen Pu, Yonatan Bisk Undergrad TAs: Sam Johnson, Nikhil](https://reader035.vdocument.in/reader035/viewer/2022062516/56649d805503460f94a64284/html5/thumbnails/1.jpg)
CS 440 / ECE 448Introduction to Artificial Intelligence
Spring 2010
Instructor: Eyal Amir
Grad TAs: Wen Pu, Yonatan Bisk
Undergrad TAs: Sam Johnson, Nikhil Johri
![Page 2: CS 440 / ECE 448 Introduction to Artificial Intelligence Spring 2010 Instructor: Eyal Amir Grad TAs: Wen Pu, Yonatan Bisk Undergrad TAs: Sam Johnson, Nikhil](https://reader035.vdocument.in/reader035/viewer/2022062516/56649d805503460f94a64284/html5/thumbnails/2.jpg)
Artificial Intelligence (AI)
Reasoning
NaturalLanguage
Learning
Vision
Knowledge
DecisionMaking
Robotics
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AI Applications
Reasoning
NaturalLanguage
Learning
Vision
Knowledge
DecisionMaking
Robotics
Medicin
Econometrics
SocialScience
Databases
Networks
AutonomousVehicles
ElectronicCommerce
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Today
• Artificial Intelligence Applications
• Artificial Intelligence Basics
• What you think you know– Logic– Probabilities– AI– Search
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What is Artificial Intelligence?
• Examples:– Game playing? (chess)– Robots? (Roomba)– Learning? (Amazon)– Autonomous space crafts? (NASA)
• What should AI have?
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Saw in Yonatan’s Presentation
• Robotics
• Vision
• A little bit of Natural-Language Processing
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Game Playing: ChessMay 1997
2006: Anthony Cozzie’s (UIUC) ZAPPA wins World Computer-Chess Championship
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Decision Making: Scrabble
Daily Illini Feb 2007:Winning computer program created by graduate student beats world champion Scrabble player
(Graduate Student = Mark Richards)
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Collaborative Filtering
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Classification
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Planning
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DARPA Grand Challenge 2003-2007
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Econometrics Example: A Recession Model of a country
– What is probability of recession, when a bank(bm) goes into bankruptcy?
– Recession: Recession of a country in [0,1]– Market[X]: Quarterly market (X) index– Loss[X,Y]: Loss of a bank (Y) in a market (X)– Revenue[Y]: Revenue of a bank (Y)
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Social Networks• Example: school friendships and their effects
Friend(A,B)
Friend(A,C)
Friend(B,C)
Attr(A)
Attr(B)
Attr(C)
Measuremt(A)
Measuremt(B)
Measuremt(C)
))(),(),(),(),(),(),,(),,(),,(Pr( CmBmAmCaBaAaCBfCAfBAf
))(),(())(),(())(),((
))(),(),,(())(),(),,(())(),(),,((1
654
321
CdCaBdBaAdAa
CaBaCBfCaAaCAfBaAaBAfZ
(.)(.),(.,.), maf61...
shorthand for Friend(., .), Atrr(.), and Measuremt(.)
potential func tions
12
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bob;joef joe;
bobf
bob;tomf tom;
bobf
joe;tomf tom;
joef
bob;annf ann;
bobf
joe;annf ann;
joef
tom;annf ann;
tomf
bob;liaf lia;
bobf
joe;liaf lia;
joef
tom;liaf lia;
tomf
ann;liaf lia;
annf bob;
valf val;
bobf
joe;valf val;
joef
tom;valf val;
tomf
ann;valf val;
annf lia;
valf val;
liaf
bbob bannbtombjoe bvalbliahbob
hannhtom
hjoe
hval
hlia
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Application: Hardware Verification
AND not
notAND
f1
f2
f3
f4
f5
OR
x1
x2
x3
f5(x1,x2,x3) = a function of the input signal
Question: Can we set this boolean cirtuit to TRUE?
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Application: Hardware Verification
AND not
notAND
f1
f2
f3
f4
f5
OR
x1
x2
x3
f5(x1,x2,x3) = f3 f4 = f1 (f2 x3) =
(x1 x2) (x2 x3)
Question: Can we set this boolean cirtuit to TRUE?
SAT(f5) ?
M[x1]=FALSEM[x2]=FALSEM[x3]=FALSE
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Finding the “best” path between two points
• Classic computer science problem: many algorithms, applications
• “best” generally means minimizing some sort of cost
s
t
source
sink
each edge has somecost associated with it
10
cost of path generally sum etc. of cost of edges along path
10
10
10
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Stochastic setting
• Edges fail probabilistically
• Goal: find most reliable path
s
t
0.95 0.9
0.85
edge reliability
path reliability = 0.95 x 0.9 x 0.85 = 0.73
assumption: independent!!!
Directed Acyclic Graph G
not very realistic...
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Stochastic setting
• Consider a richer structure using a graphical model
s
t
e1e2
e3
binary random variables:1 if edge survives, 0 if edge fails
X(discrete) hidden variable
the hidden variable allows us to model correlations and dependencies between edge failures
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Stochastic setting
• Specified:– prior probability on X– conditional probabilities for each edge
s
t
e1e2
e3
X
Pr[X=1] = 0.4Pr[X=2] = 0.1Pr[X=3] = 0.2Pr[X=4] = 0.3
Pr[e1 survives | X=1] = 0.9Pr[e1 fails | X=1] = 0.1... etc.
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Stochastic setting
• Graphical model defines joint distribution:
Pr[X,e1,e2,e3,...]= Pr[X] Pr[e1|X] Pr[e2|X]...
• Reliability of path is marginal Pr[e1,e2,e3]
• Can compute by summing...
s
t
e1e2
e3
X
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Many applications
• Just to name a few:– Network QoS routing [citations]
links fail stochastically
routers fail stochastically
Failures are typically correlated: if two machines run the same version ofunpatched Windows, and one gets infected by a virus...
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Many applications
• Just to name a few:– Network QoS routing [citations]– Parsing w/ weighted FSAs
(from Smith + Eisner ACL’05 best paper)
FSA where edges have probabilities assigned to them
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Many applications
• Just to name a few:– Network QoS routing– Parsing w/ weighted FSAs– Robot navigation
e.g., DARPA Grand Challenge
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Motivation of AI
• Autonomous computers
• Embedded computers
• Programming by telling
• Human-like capabilities – vision, natural language, motion and manipulation
• Applications: learning, media, www, manipulation, verification, robots, cars, help for disabled, dangerous tasks
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Long-Term Goals
• Computers that can accept advice• Programs that process rich information
about the everyday world• Programs that can replace experts• Computer programs that can decide on
actions: control, planning, experimentation• Programs that combine knowledge of
different types and sources• Programs that learn
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Short-Term Goals
• Knowledge & reasoning – acquire, represent, use, answer questions
• Planning & decision making
• Diagnosis & analysis
• Learning, pattern recognition
• Inferring state of the world from sensors– Vision– Natural-language text
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What This Course Covers
• Major techniques in artificial intelligence– Search in large spaces and game search– Logical reasoning– Planning and sequential decision making– Knowledge representation - logic & probability– Probabilistic reasoning– Machine Learning– Robotic control, Stimulus-Response– Machine Vision
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What you should know
• Matrix Algebra
• Probability and Statistics
• Logic
• Data structures
• C++, Java, Python, or Matlab
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What You Will Know
• Matlab
• Building and reasoning with complex probabilistic and logical knowledge
• Build autonomous agents
• Create vision/sensing routines for simple detection, identification, and tracking
• Create programs that make decisions autonomously or semi-autonomously
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Administration
• Office Hours, Late policy, homework deadlines, syllabus, and how to make a home-cooked meal – check the website:
http://www.cs.uiuc.edu/class/cs440