influence diagram
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Influence Diagram
主講人:虞台文
大同大學資工所智慧型多媒體研究室
Content
IntroductionDefinition & Optimal PolicyFinding Optimal Policy
Influence Diagram
Introduction
大同大學資工所智慧型多媒體研究室
Decision Analysis
Decision Tree --- Traditional approaches– Tree size grow exponentially with the number of deci
sions and attributes
Influence Diagram– Introduced by Harward and Matheson on 1984.– An extension of Bayesian Networks– More compact than decision tree– Reveal more problem structure in decision making
Influence Diagrams
An influence Diagram is an acyclic graph with three types of nodes– Random nodes
• A Bayesian Network only contains random nodes
– Decision nodes– Value nodes
Example
Sick’ Dry’
Loses’
Sick Dry
Loses
Treat
Harv
Cost
Random node
Decision node
Value node
Example
Sick’ Dry’
Loses’
Sick Dry
Loses
Treat
Harv
Cost
Sick Probability
sick 0.1not 0.9
Dry Probability
dry 0.1not 0.9
Losesdry not
sick not sick not
yes 0.95 0.85 0.9 0.02
not 0.05 0.15 0.1 0.98
Example
Sick’ Dry’
Loses’
Sick Dry
Loses
Treat
Harv
Cost
Sick’treat not
sick not sick not
sick 0.2 0.01 0.99 0.02
not 0.8 0.99 0.01 0.98
Treattreatnot
Dry’ dry not
dry 0.6 0.05
not 0.4 0.95Loses’
dry not
sick not sick not
yes 0.95 0.9 0.85 0.02
not 0.05 0.1 0.15 0.98
Example
Sick’ Dry’
Loses’
Sick Dry
Loses
Treat
Harv
Cost
Treat treat not
Cost/Utility -8000 0
Harv sick not
Harv/Utility 3000 20000
Example
Sick’ Dry’
Loses’
Sick Dry
Loses
Treat
Harv
Cost
Observe that the apple tree loses leaves.
Whether the tree will be treated?
DemoDemo
Influence Diagram
Definition & Optimal Policy
大同大學資工所智慧型多媒體研究室
Nodes of Influence Diagrams
Random (Chance) nodes– Each represent a random variable whose value is dictated by
some probability distribution.– Each is associated with a conditional probability distribution.
Decision nodes– Each represents decision variable whose value is to be
chosen by the decision maker.– Each is associated with a policy.
Value nodes– Each represents a real-valued utility function– Can’t have child.
( )vvf
Arcs of Influence Diagrams
Conditional Arcs– Arcs into random nodes.
Informational Arcs– Arcs into decision nodes.
Notations
x
v
X DC U The set of all value nodes
The set of all decision nodes
The set of all chance nodes
: Parents of x X.
: The frame of v CD.
, J xx JJ C D
Random Nodes
ci
. . .
c C D Conditional
Arcs
( | ), i ii c i cP c c
Decision Nodes
. . .id C D
InformationalArcs
di
Decision function
:d ii
i d Depends on policy
What is the optimal policy?
How many policies we may choose?
That is, what is the size of decision space?
Decision Nodes
. . .id C D
InformationalArcs
di
Decision function
:d ii
i d
A BN with decision nodes d1,…,dk.
Policy 1( , , )k
Given a policy, each decision nodes can be converted to a random node by
1 if ( )( | )
0 otherwisei
i i
i d ii d
dP d
Decision Nodes
. . .id C D
InformationalArcs
di
Decision function
:d ii
i d
A BN with decision nodes d1,…,dk.
Policy 1( , , )k
Given a policy, each decision nodes can be converted to a random node by
1 if ( )( | )
0 otherwisei
i i
i d ii d
dP d
Thus, given a policy , an inference diagram with random nodes and decision nodes only can be viewed as a BN.
Thus, given a policy , an inference diagram with random nodes and decision nodes only can be viewed as a BN.
Probability Evaluation
Given a policy, say, =(1, …, k), the probability with nodes CD is then
1 if ( )( | )
0 otherwisei
i i
i d ii d
dP d
( , ) ( | ) ( | )d Dc
c dC
P P PD dC c
A CD then
( ) ( , )D AC
CP A DP
Utility Nodes
. . .iv C D
Utility function
:i vi
vf R vi
iv U
Expected Values
Given a policy =(1, …, k), the expected value of utility node v in the influence diagram is
:i vi
vf R
( )[ (] )v
v v vfE Pv
The expected value of the influence diagram N is
[ ] ( )v U
E E v
N
Optimal Expected Value
[ ] ( )v U
E E v
N
OptimalExpected Value
[ ] max [ ]E E N N
Optimal Policy * arg max [ ]E N
*[ ] [ ]E E N NHow?
Influence Diagram
Finding Optimal Policy
大同大學資工所智慧型多媒體研究室
Shachter and Peot’s Transformation
Convert a value node v to a binary random node
( )[ (] )v
v v vfE Pv
( )( 1| ) v v
vv
fP v
M
max ( )vv v vM f
Given a policy, denote the reformulated BN as N.
Shachter and Peot’s Transformation
( )[ (] )v
v v vfE Pv
( )( 1| ) v v
vv
fP v
M
( )
( 1| ) v vv
v
fP v
M
{0,1}
[ ] ( )v
E v vP v
N N 0 ( 0) 1 ( 1)P v P v
N N
( 1)P v
N
. . .
v
( 1| ) ( )v
v vP v P
1
( ) ( )v
v v vv
f PM
1
( )v
E vM ( ) ( 1) vE v P v M
N( ) ( 1) vE v P v M
N
Shachter and Peot’s Transformation
[ ] [ ]v U
E E v
N
( ) ( 1) vE v P v M N
( ) ( 1) vE v P v M N
( 1) vv U
MP v
N
[ ] max [ ]E E v N (max 1)v U
vP v M
N
Evaluating the expected value for a given policy:
Evaluating the expected value for an optimal policy:
Bucket Elimination Algorithm
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