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Today

- Applications of reasoning in AI
- Econometrics
- Social Networks
- Verification of Circuits and Programs
- Natural Language Processing
- Robotics
- Vision
- Computer Security

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)

Social Networks

Example: school friendships and their effects

Friend(A,B)

Attr(A)

Measuremt(A)

shorthand for Friend(., .), Atrr(.), and Measuremt(.)

potential functions

Friend(A,C)

Attr(B)

Measuremt(B)

Friend(B,C)

Attr(C)

Measuremt(C)

blia

hjoe

htom

hbob

btom

hann

bjoe

bann

bbob

hval

bval

f

f

f

f

f

f

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f

f

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f

f

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f

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tom;

val

bob;

lia

lia;

tom

ann;

lia

ann;

tom

lia;

ann

val;

bob

tom;

ann

joe;

val

ann;

joe

val;

joe

tom;

lia

bob;

val

lia;

joe

bob;

tom

val;

tom

joe;

ann

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bob

val;

lia

joe;

bob

tom;

bob

joe;

tom

tom;

joe

joe;

lia

bob;

ann

lia;

val

val;

ann

ann;

val

lia;

bob

bob;

joe

Scaling-Up: Computing Pr(f(x,y))

Figure 5: Computation time for

Application: Hardware Verification

f3

x1

f1

not

AND

x2

f5

AND

not

f2

OR

x3

f4

Question: Can we set this boolean cirtuit to TRUE?

f5(x1,x2,x3) = a function of the input signal

Application: Hardware Verification

f3

x1

f1

not

AND

x2

f5

AND

not

f2

OR

SAT(f5) ?

x3

f4

Question: Can we set this boolean cirtuit to TRUE?

f5(x1,x2,x3) = f3 f4 = f1 (f2 x3) =

(x1 x2) (x2 x3)

M[x1]=FALSE

M[x2]=FALSE

M[x3]=FALSE

Hardware Verification

- Questions in logical circuit verification
- Equivalence of circuits
- Arrival of the circuit to a state (required a temporal model, which gets propositionalized)
- Achieving an output from the circuit

Natural-Language Processing

- Logical semantics
- Probabilistic choice between meanings
- Inference over time

Robotics

- Videos

Vision

- Videos

Computer Security

- Shortest paths

Finding the “best” path between two points

- Classic computer science problem: many algorithms, applications
- “best” generally means minimizing some sort of cost

each edge has some

cost associated with it

cost of path generally sum etc. of cost of edges along path

10

10

10

source

s

10

sink

t

Stochastic setting

- Edges fail probabilistically
- Goal: find most reliable path

Directed Acyclic Graph G

edge reliability

t

0.85

s

0.9

0.95

path reliability = 0.95 x 0.9 x 0.85 = 0.73

assumption: independent!!!

not very realistic...

Stochastic setting

- Consider a richer structure using a graphical model

(discrete) hidden variable

X

t

e3

s

e2

e1

the hidden variable allows us to model correlations and dependencies between edge failures

binary random variables:

1 if edge survives, 0 if edge fails

Stochastic setting

- Specified:
- prior probability on X
- conditional probabilities for each edge

Pr[X=1] = 0.4

Pr[X=2] = 0.1

Pr[X=3] = 0.2

Pr[X=4] = 0.3

Pr[e1 survives | X=1] = 0.9

Pr[e1 fails | X=1] = 0.1

... etc.

X

t

e3

s

e2

e1

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...

X

t

e3

s

e2

e1

Many applications

- Just to name a few:
- Network QoS routing[citations]

routers fail stochastically

links fail stochastically

Failures are typically correlated: if two machines run the same version of

unpatched Windows, and one gets infected by a virus...

Many applications

- Just to name a few:
- Network QoS routing [citations]
- Parsing w/ weighted FSAs

FSA where edges have probabilities assigned to them

(from Smith + Eisner ACL’05 best paper)

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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