Submodularity for Distributed Sensing
Problems
Zeyn Saigol
IR Lab, School of Computer Science
University of Birmingham
6th July 2010
Outline
1.
2.
3.
4.
5.
What is submodularity?
Non-myopic maximisation of information gain
Path planning
Robust maximisation and minimisation
Summary
* These slides are borrowed from Andreas Krause and Carlos
Guestrin (see http://www.submodularity.org/)
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Set functions

Submodularity in AI has been popularised by
Andreas Krause and Carlos Guestrin
 Finite set V = {1,2,…,n}
 Function F: 2V  R

Example: F(A)
X1
“Fever”
= IG(XA; Y) = H(Y) – H(Y | XA)
= y,xA P(xA) [log P(y | xA) – log P(y)]
Y
“Sick”
Y
“Sick”
X2
“Rash”
X2
“Rash”
F({X1,X2}) = 0.9
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X3
“Male”
F({X2,X3}) = 0.5
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Submodular set functions

Set function F on V is called submodular if
For all A,B  V: F(A)+F(B)  F(AB)+F(AB)
+
A A BB

+
AB

Equivalent diminishing returns characterization:
Submodularity:
B A
+ S
Large improvement
+ S
Small improvement
For AB, sB, F(A {s}) – F(A)  F(B {s}) – F(B)
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Example problem: sensor coverage
Place sensors
in building
Node predicts
values of positions
with some radius
Possible
locations
V
For A  V: F(A) = “area
covered by sensors placed at A”
Formally:
W finite set, collection of n subsets Si  W
For A  V={1,…,n} define F(A) = |iA Si|
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Set coverage is submodular
A={S1,S2}
S1
S2
S’
S’
F(A{S’})-F(A)
≥
S1
F(B{S’})-F(B)
S2
S3
S4
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S’
B = {S1,S2,S3,S4}
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Approximate maximization
Given: finite set V, monotonic submodular function F(A)
Want:
A*  V such that
NP-hard!
Y
“Sick”
Greedy algorithm:
M
X1
Start with A0 = {};
“Fever”
For i = 1 to k
si := argmaxs F(Ai-1{s})-F(Ai-1)
Ai := Ai-1{si}
IRLab, 6 Jul 2010
Submodularity
X2
“Rash”
X3
“Male”
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Guarantee on greedy algorithm
Theorem [Nemhauser et al ‘78]
Given a monotonic submodular function F, F()=0,
the greedy maximization algorithm returns Agreedy
F(Agreedy)  (1-1/e) max|A| k F(A)
~63%
Sidenote: Greedy algorithm gives
1/2 approximation for
maximization over any matroid C!
[Fisher et al ’78]
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Example: Submodularity of info-gain
Y1,…,Ym, X1, …, Xn discrete RVs
F(A) = IG(Y; XA) = H(Y)-H(Y | XA)
 F(A) is always monotonic
 However, NOT always submodular
Theorem [Krause & Guestrin UAI’ 05]
If Xi are all conditionally independent given Y,
then F(A) is submodular!
Y1
X1
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Y2
X2
X3
Y3
Hence, greedy algorithm works!
X4
In fact, NO practical algorithm can do
better than (1-1/e) approximation! 9
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Information gain with cost

Instead of each sensor having the same
measurement cost, variable cost C(X) for each node
 Aim: max F(A) s.t. C(A)  B
where C(A)=XAC(X)
 In this case, construct every possible 3-element
subset of V, and run greedy algorithm on each
 Greedy algorithm selects additional nodes X by
maximising F ( A  X )  F ( A )
C(X )

Finally choose best set A
 Maintains (1 − 1/e) approximation guarantee
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Path planning
maxA F(A) or maxA mini Fi(A) subject to


So far: |A|  k
In practice, more complex constraints:
Locations need to be
connected by paths
Sensors need to communicate
(form a routing tree)
[Krause et al., IPSN 06]
[Chekuri & Pal, FOCS ’05]
[Singh et al, IJCAI ’07]
Lake monitoring
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Building monitoring
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Informative path planning
So far:
max F(A) s.t. |A| k
Most informative locations
Robot needs to travel
might be far apart!
between selected locations
s4
2
2
s2
1
s10
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s1
1
1
1
s11
1
1
s3
s5
Locations V nodes in a graph
C(A) = cost of cheapest path
connecting nodes A
max F(A) s.t. C(A)  B
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The pSPIEL Algorithm [K, Guestrin, Gupta, Kleinberg IPSN ‘06]

pSPIEL: Efficient nonmyopic algorithm
(padded Sensor Placements at Informative and costEffective Locations)
g2,2
g1,2
g1,1
g1,3
g2,1
C1
g2,3
C2
S1
SB
g4,3
g4,1
g4,4
g3,1
g3,3
C4
g4,2
IRLab, 6 Jul 2010
g3,2
g3,4
C3
Select starting and ending
location s1 and sB
Decompose sensing region into
small, well-separated clusters
Solve cardinality constrained
problem per cluster (greedy)
Combine solutions using
orienteering algorithm
Smooth resulting path
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Limitations of pSPEIL
Requires locality property – far apart observations
are independent
 Adaptive algorithm [Singh, Krause, Kaiser
IJCAI‘09]



Just re-plans on every timestep
Often this is near-optimal; if it isn’t, have to add an
adaptivity gap term to the objective function U to
encourage exploration
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Submodular optimisation spectrum

Maximization: A* = argmax F(A)




Sensor placement
Informative path planning
Active learning
…

Optimise for worst case:

Minimization: A* = argmin F(A)



Structure learning (A* = argmin I(XA; XVnA))
Clustering
MAP inference in Markov Random Fields
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Summary

Submodularity is useful!
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