lectures 08 decision making

8/21/2019 Lectures 08 Decision Making

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Fahiem Bacchus, University of Toronto1

Decision Making Under

Uncertainty.

Chapter 16 in your text.

CSC384h: Intro to Artificial Intelligence

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Preferences

I give robot a planning problem: I want coffee

but coffee maker is broken: robot reports “No plan!”



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Preferences

We really want more robust behavior. Robot to know what to do if my primary goal can’t

be satisfied – I should provide it with some indication

of my p re fe re nc e s o ve r a lte rna t ive s e.g., coffee better than tea, tea better than water,

water better than nothing, etc.

But it’s more complex: it could wait 45 minutes for coffee maker to be fixed

what’s better: tea now? coffee in 45 minutes?

could express preferences for <beverage,time> pairs



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Preference Orderings

A p re fe re nc e o rd e ring ≽ is a ranking of all

possible states of affairs (worlds) S

these could be outcomes of actions, truth assts,states in a search problem, etc.

s≽ t: means that state s is a t le a st a s g o o d a s t

s≻ t: means that state s is stric t ly p re fe rre d to t

We insist that ≽ is

reflexive: i.e., s ≽ s for all states s transitive: i.e., if s≽ t and t ≽ w, then s ≽ w

connected: for all states s,t, either s≽ t or t ≽ s



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Why Impose These Conditions?

Structure of preference orderingimposes certain “rationality

requirements” (it is a weak ordering)E.g., why transitivity?

Suppose you (strictly) prefer coffee to

tea, tea to OJ , OJ to coffee If you prefer X to Y, you’ll trade me Y

plus $1 for X

I can construct a “money pump” andextract arbitrary amounts of moneyfrom you


≻

≻

≻

Best

Worst


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Decision Problems: Certainty

A d e c isio n p ro b lem und e r c e rta in ty is: a set of decis ions D

e.g., paths in search graph, plans, actions… a set of o u t c ome s or states S

e.g., states you could reach by executing a plan

an o u tc om e func t io n f : D→

S the outcome of any decision

a preference ordering ≽ over S

A solution to a decision problem is any d*∊ Dsuch that f(d*) ≽ f(d) for all d∊D



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Decision Problems: Certainty

A d e c isio n p ro b lem und e r c e rta in ty is: a set of decis ions D

a set of o u t c ome s or states S

an ou t c om e func t ion f : D →S

a preference ordering ≽ over S

A solution to a decision problem is any d*∊ D such

that f(d*) ≽ f(d) for all d∊D

e.g., in classical planning we that any goal state s ispreferred/equal to every other state. So d* is a solution iff

f(d*) is a solution state. I.e., d* is a solution iff it is a plan thatachieves the goal.

More generally, in classical planning we might considerdifferent goals with different values, and we want d* to be

a plan that optimizes our value.



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Decision Making under Uncertainty

Suppose actions don’t have deterministic outcomes e.g., when robot pours coffee, it spills 20% of time, making a

mess preferences: chc, ¬mess≻ ¬chc,¬mess≻ ¬chc, mess

What should robot do? decision ge t co f f ee leads to a good outcome and a bad

outcome with some probability

decision dono th ing leads to a medium outcome for sure Should robot be optimistic? pessimistic? Really odds of success should influence decision

but how?


getcoffee

chc, ¬mess

¬chc, mess

donothing ¬chc, ¬mess.8

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Utilities

Rather than just ranking outcomes, we mustquantify our degree of preference

e.g., how much more important is having coffeethan having tea?

A u tility func tio n U: S→ℝ associates a real-

valued utility with each outcome (state). U(s) quantifies our degree of preference for s

Note: U induces a preference ordering ≽U over

the states S defined as: s≽U t iff U(s) ≥ U(t)

≽U is reflexive, transitive, connected



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Expected Utility

With utilities we can compute expected utilities!

In decision making under uncertainty, each

decision d induces a distribution Prd overpossible outcomes

Prd(s) is probability of outcome s under decision d

The e xp e c te d ut ilit y of decision d is defined


∑∈

=

S s

d sU sd EU )()(Pr )(


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Expected Utility

Say U(chc,¬ms) = 10, U(¬chc,¬ms) = 5,U(¬chc,ms) = 0,

Then EU(getcoffee) = 8

EU(donothing) = 5

If U(chc,¬ms) = 10, U(¬chc,¬ms) = 9,U(¬chc,ms) = 0,

EU(getcoffee) = 8

EU(donothing) = 9


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The MEU Principle

The p rinc ip le o f m a xim um e xp e c te d ut ilit y(MEU) states that the optimal decision under

conditions of uncertainty is the decision that hasgreatest expected utility.

In our example

if my utility function is the first one, my robot shouldget coffee

if your utility function is the second one, your robot

should do nothing



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Computational Issues

At some level, solution to a dec. prob. is trivial complexity lies in the fact that the decisions and outcome

function are rarely specified explicitly

e.g., in planning or search problem, you const ruct the set ofdecisions by constructing paths or exploring search paths. Then we have to evaluate the expected utility of each.Computationally hard!

e.g., we find a plan achieving some expected utility e

Can we stop searching?

Must convince ourselves no better plan exists

Generally requires searching entire plan space, unlesswe have some c lever tricks



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Decision Problems: Uncertainty

A d e c isio n p ro b lem und e r unc e rta in ty is: a set of decis ions D

a set of o u t c ome s or states S an o u tc om e func t io n Pr : D →Δ(S)

Δ(S) is the set of distributions over S (e.g., Prd)

a utility function U over SA solution to a decision problem under

uncertainty is any d*∊ D such that EU(d*) ≽ EU(d)

for all d∊D


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Expected Utility: Notes

Note that this viewpoint accounts for both:

uncertainty in action outcomes

uncertainty in state of knowledge any combination of the two


s0

s1

s2a

0.80.2

s3

s4

b 0.30.7

0.7 s1

0.3 s2

0.7 t1

0.3 t2

0.7 w1

0.3 w2

a

b

Stochastic actions Uncertain knowledge


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Expected Utility: Notes

Why MEU? Where do utilities come from?

underlying foundations of utility theory tightlycouple utility with action/choice

a utility function can be determined by askingsomeone about their preferences for actions inspecific scenarios (or “lotteries” over outcomes)

Utility functions needn’t be unique

if I multiply U by a positive constant, all decisionshave same relative utility

if I add a constant to U, same thing

U is un iq ue up to p o sit ive a ffine tra nsfo rm a t io n



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So What are the Complications?

Outcome space is large

like all of our problems, states spaces can be huge

don’t want to spell out distributions like Prd explicitly Soln: Bayes nets (or related: in flue nc e d ia g ra m s )



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So What are the Complications?

Decision space is large

usually our decisions are not one-shot actions

rather they involve sequential choices (like plans) if we treat each plan as a distinct decision, decision

space is too large to handle directly

Soln: use dynamic programming methods toconst ruct optimal plans (actually generalizations ofplans, called policies… like in game trees)



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An Simple Example

Suppose we have two actions: a, b

We have time to execute tw o actions in

sequence This means we can do either:

[a,a], [a,b], [b,a], [b,b]

Actions are stochastic: action a inducesdistribution Pra(si | s j) over states

e.g., Pra(s2 | s1) = .9 means prob. of moving to state

s2 when a is performed at s1 is .9 similar distribution for action b

How good is a particular sequence of actions?



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Distributions for Action Sequences


s1

s13s12s3s2

a b

.9 .1 .2 .8

s4 s5

.5 .5

s6 s7

.6 .4

a b

s8 s9

.2 .8

s10 s11

.7 .3

a b

s14 s15

.1 .9

s16 s17

.2 .8

a b

s18 s19

.2 .8

s20 s21

.7 .3

a b

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Distributions for Action Sequences

Sequence [a,a] gives distribution over “final states” Pr(s4) = .45, Pr(s5) = .45, Pr(s8) = .02, Pr(s9) = .08

Similarly: [a,b]: Pr(s6) = .54, Pr(s7) = .36, Pr(s10) = .07, Pr(s11) = .03 and similar distributions for sequences [b,a] and [b,b]


s1

s13s12s3s2

a b

.9 .1 .2 .8

s4 s5

.5 .5

s6 s7

.6 .4

a b

s8 s9

.2 .8

s10 s11

.7 .3

a b

s14 s15

.1 .9

s16 s17

.2 .8

a b

s18 s19

.2 .8

s20 s21

.7 .3

a b

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How Good is a Sequence?

We associate ut ilit ie s w ith the “fina l” o utc om e s how good is it to end up at s4, s5, s6, …

Now we have: EU(aa) = .45u(s4) + .45u(s5) + .02u(s8) + .08u(s9)

EU(ab) = .54u(s6) + .36u(s7) + .07u(s10) + .03u(s11)

etc…



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Utilities for Action Sequences


s1

s13s12s3s2

a b

.9 .1 .2 .8

u(s4) u(s5)

.5 .5u(s6)

.6 .4

a b

.2 .8 .7 .3

a b

.1 .9 .2 .8

a b

.2 .8u(s21)

.7 .3

a b

etc….Looks a lot like a game tree, but with chance nodes

instead of min nodes. (We average instead of minimizing)


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Ac t io n Se q uenc e s are not sufficient

Suppose we do a first; we could reach s2 or s3: At s2, assume: EU(a) = .5u(s4) + .5u(s5) > EU(b) = .6u(s6) + .4u(s7)

At s3: EU(a) = .2u(s8) + .8u(s9) < EU(b) = .7u(s10) + .3u(s11)

After doing a first, we want to do a next if w e re a c h s2 , but wewant to do b second if w e re a c h s3


s1

s13s12s3s2

a b

.9 .1 .2 .8

s4 s5

.5 .5

s6 s7

.6 .4

a b

s8 s9

.2 .8

s10 s11

.7 .3

a b

s14 s15

.1 .9

s16 s17

.2 .8

a b

s18 s19

.2 .8

s20 s21

.7 .3

a b


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Policies

This suggests that when dealing with uncertaintywe want to consider pol ic ies , not just sequencesof actions (plans)

We have eight policies for this decision tree:

[a; if s2 a, if s3 a] [b; if s12 a, if s13 a]

[a; if s2 a, if s3 b] [b; if s12 a, if s13 b][a; if s2 b, if s3 a] [b; if s12 b, if s13 a]

[a; if s2 b, if s3 b] [b; if s12 b, if s13 b]

Contrast this with four “plans” [a; a], [a; b], [b; a], [b; b]

note: each plan corresponds to a policy, so we can

only ga in by allowing decision maker to use policiesFahiem Bacchus, University of Toronto25

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Evaluating Policies

Number of plans (sequences) of length k exponential in k : | A | k if A is our action set

Number of policies is even larger if we have n=| A | actions and m=| O | outcomes

per action, then we have ( nm) k policies

Fortunately, d ynam ic p ro g ra mm ing can be used e.g., suppose EU(a) > EU(b) at s2

never consider a policy that does anything else at

s2How to do this?

back values up the tree much like minimax search





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Decision TreesSquares denote cho i ce nodes

these denote action choicesby decision maker (decis ionnodes )

Circles denote c h a n c e nodes these denote uncertainty

regarding action effects “Nature” will choose the child

with specified probability

Terminal nodes labeled withutilities

denote utility of final state (or itcould denote the utility of“trajectory” (branch) todecision maker


s1a

b

.9 .1 .2 .8

5 2 4 3




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Evaluating Decision Trees

Procedure is exactly like game trees, except… key difference: the “opponent” is “nature” who

simply chooses outcomes at chance nodes with

specified probability: so we take expectationsinstead of minimizing

Back values up the tree

U(t) is defined for all terminals (part of input) U(n) =exp {U(c ) : c a child of n } if n is a chance

node

U(n) =max {U(c ) : c a child of n } if n is a choicenode

At any choice node (state), the decision makerchooses action that leads to h ig he st u tility c h ild





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Evaluating a Decision Tree

U(n3) = .9*5 + .1*2

U(n4) = .8*3 + .2*4

U(s2) = max{U(n3), U(n4)} decision a or b (whichever is

max)

U(n1) = .3U(s2) + .7U(s3)U(s1) = max{U(n1), U(n2)}

decision: max of a, b


s2

n3a b

.9 .1

5 2

n4.8 .2

3 4

s1

n1

a b

.3 .7n2

s3

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Decision Tree Policies

Note that we don’t just compute values,but policies for the

treeA p o lic y assigns adecision to each

choice node in tree


Some policies can’t be distinguished in terms of theirexpected values

e.g., if policy chooses a at node s1, choice at s4 doesn’tmatter because it won’t be reached

Two policies are im p lem enta t io na lly ind ist ing uisha b le if theydisagree only at unreachable decision nodes

reachability is determined by policy themselves

s2

n3

a b

n4

s1

n1

a b

.3 .7n2

s3 s4

a bab




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Key Assumption: Observability

Full observability:we must know the initial stateand outcome of each action

specifically, to implement the policy,w e m ust b ea b le to re so lve the unc e rta in ty o f a ny c ha nc e no d e

tha t is fo llo w e d b y a d e c isio n no d e

e.g., after doing a at s1, we must know which of the

outcomes (s2 or s3) was realized so we know whataction to do next (note: s2 and s3 may prescribedifferent ations)





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Savings compared to explicit policy evaluation issubstantial

Evaluate only O(( nm) d ) nodes in tree of depth d total computational cost is thus O(( nm) d )

Note that this is how many p o lic ie s there are but evaluating a single policy explicitly requires

substantial computation: O(nm d ) total computation for explicity evaluating each

policy would be O(n d m 2d ) !!!

Tremendous value to dynamic programmingsolution


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Tree size:grows exponentially with depth

Possible solutions:

bounded lookahead with heuristics (like gametrees)

heuristic search procedures (like A*)


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Other Issues

Specification:suppose each state is anassignment to variables; then representingaction probability distributions is complex (andbranching factor could be immense)

Possible solutions:

represent distribution using Bayes nets solve problems using d e c isio n ne tw o rks (or

influence diagrams)


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Large State Spaces (Variables)

To represent outcomes of actions or decisions,we need to specify distributions

Pr(s| d) : probability of outcome s given decision d

Pr(s| a,s’): prob. of state s given that action aperformed in state s’

But state space exponential in # of variables spelling out distributions explicitly is intractable

Bayes nets can be used to represent actions

this is just a joint distribution over variables,conditioned on action/decision and previous state


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Decision Networks

De c isio n ne tw o rks (more commonly known asin flue nc e d ia g ra m s ) provide a way ofrepresenting sequential decision problems

basic idea: represent the variables in the problemas you would in a BN

add decision variables – variables that you

“control”

add utility variables – how good different states are


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Sample Decision Network


Disease

TstResult

Chills

Fever

BloodTst Drug

U

optional

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Decision Networks: Decision Nodes

Decision nodes variables decision maker sets, denoted by squares

parents reflect in fo rm a t io n a va ila b le at time

decision is to be made In example decision node: the actual values ofCh and Fev will be observed before the decision

to take test must be made agent can make d if fe re nt d e c isio ns for each

instantiation of parents


Chills

FeverBloodTst BT ∊ {bt, ¬bt}

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Decision Networks: Value Node

Value node

specifies utility of a state, denoted by a diamond

utility dependso n ly o n sta te o f p a re n ts of valuenode

generally: only one value node in a decisionnetwork

Utility depends only on disease and drug


Disease

BloodTst Drug

U

optional

U(fludrug, flu) = 20U(fludrug, mal) = -300

U(fludrug, none) = -5U(maldrug, flu) = -30U(maldrug, mal) = 10U(maldrug, none) = -20U(no drug, flu) = -10

U(no drug, mal) = -285U(no drug, none) = 30

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Decision Networks: Assumptions

Decision nodes are totally ordered decision variables D1, D2, …, Dn

decisions are made in sequence

e.g., BloodTst (yes,no) decided before Drug(fd,md,no)


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Decision Networks: Assumptions

No -fo rg e t t ing p ro p e rty any information available when decision Di is made

is available when decision D j is made (for i < j)

thus all parents of Di are parents of D j Network does not show these “implicit parents”, but the

links are present, and must be considered whenspecifying the network parameters, and when

computing.


Chills

Fever

BloodTst DrugDashed arcsensure theno-forgettingproperty

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P li i

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Policies

Let Par(D i ) be the parents of decision node D i Dom(Par(D i )) is the set of assignments to parents

A policyδ

is a set of mappingsδ i , one for eachdecision node D i

δ i :Dom(Par(D i )) →Dom(D i )

δ i associates a decision with each parent asst forD i

For example, a policy for BT might be: δ BT (c ,f) = b t

δ BT (c ,¬f) = ¬b t δ BT (¬c ,f) = b t δ BT (¬c ,¬f) = ¬b t


Chills

FeverBloodTst

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V l f P li

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Value of a Policy

Va lue o f a p o lic y δ is the expected utility giventhat decision nodes are executed according toδ

Given asst x to the set X of all chance variables,let δ (x) denote the asst to decision variablesdictated by δ

e.g., asst to D 1 determined by it’s parents’ asst in x e.g., asst to D 2 determined by it’s parents’ asst in x

along with whatever was assigned to D 1 etc.

Value of δ : EU(δ ) =ΣX P(X, δ (X)) U(X, δ (X))


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O ti l P li i

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Optimal Policies

An o p t im a l p o lic y is a policy δ * such thatEU(δ* ) ≥ EU(δ ) for all policiesδ

We can use the dynamic programming principleto avoid enumerating all policies

We can also use the structure of the decision

network to use variable elimination to aid in thecomputation


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C ti th B t P li

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Computing the Best Policy

We can work backwards as follows

First compute optimal policy for Drug (last dec’n)

for each asst to parents (C,F,BT,TR) and for eachdecision value (D = md,fd,none), c om p ute theexp ec ted va lue of choosing that value of D

set policy choice for each

value of parents to bethe value of D thathas max value

eg: δ D (c ,f,b t ,p o s) = m d


Disease

TstResultChills

FeverBloodTst Drug

U

optional

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Next compute policy for BT given policyδ D (C ,F,BT,TR) just determined for Drug

since δ D (C ,F,BT,TR) is fixed, we can treat Drug as a

normal random variable with deterministicprobabilities

i.e., for any instantiation of parents, value of Drug is

fixed by policy δ D

this means we can solve for optimal policy for BT justas before

only uninstantiated vars are random vars (once wefix its parents)


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Computing the Best Policy How do we compute these expected values?

suppose we have asst <c,f ,bt ,pos> to parents of Drug we want to compute EU of deciding to set Drug = m d we can run variable elimination!

Treat C ,F,BT,TR,D r as evidence this reduces factors (e.g., U restricted to b t ,md : depends onDis )

eliminate remaining variables (e.g., only Dise a se left)

left with factor:U() =ΣDis P(Dis| c,f,bt,pos,md)U(Dis)


Disease

TstResult

Chills

Fever

BloodTst Drug

U

optional

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We now know EU of doing Dr=md when c , f ,b t ,pos true

Can do same for fd ,no to

decide which is best


Disease

TstResult

Chills

Fever

BloodTst Drug

U

optional

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Computing Expected Utilities

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Computing Expected Utilities

The preceding illustrates a general phenomenon computing expected utilities with BNs is quite easy

utility nodes are just factors that can be dealt withusing variable elimination

EU =ΣA,B,C P(A,B,C) U(B,C)

=ΣA,B,C P(C| B) P(B| A) P(A) U(B,C) J ust eliminate variables

in the usual way


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Optimizing Policies: Key Points

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If a decision node D has no decisions that followit, we can find its policy by instantiating each ofits parents and computing the expected utility of

each decision for each parent instantiation


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no-forgetting means that all other decisions areinstantiated (they must be parents)

its easy to compute the expected utility using VE

the number of computations is quite large: we runexpected utility calculations (VE) for each parent

instantiation together with each possible decision Dmight allow

policy: choose max decision for each parent

instant’n


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When a decision D node is optimized, it can betreated as a random variable

for each instantiation of its parents we now know

what value the decision should take just treat policy as a new CPT: for a given parent

instantiation x, D getsδ (x) with probability 1(allother decisions get probability zero)

If we optimize from last decision to first, at eachpoint we can optimize a specific decision by (abunch of) simple VE calculations

it’s successor decisions (optimized) are just normalnodes in the BNs (with CPTs)


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Decision Network Notes

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Decision Network Notes

Decision networks commonly used by decisionanalysts to help structure decision problems

Much work put into computationally effectivetechniques to solve these

Complexity much greater than BN inference

we need to solve a number of BN inferenceproblems

one BN problem for each setting of decision nodeparents and decision node value


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Real Estate Investment

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Real Estate Investment


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DBN-Decision Nets for Planning

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DBN Decision Nets for Planning


T t

Lt

Ct

Rt

T t+1

Lt+1

Ct+1

Rt+1

Mt Mt+1

Actt

U

T t-1

Lt-1

Ct-1

Rt-1

Mt-1

Actt-1

T t-2

Lt-2

Ct-2

Rt-2

Mt-2

Actt-2

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A Detailed Decision Net Example

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Setting: you want to buy a used car, but there’sa good chance it is a “lemon” (i.e., prone tobreakdown). Before deciding to buy it, you can

take it to a mechanic for inspection. They willgive you a report on the car, labeling it either“good” or “bad”. A good report is positivelycorrelated with the car being sound, while abad report is positively correlated with the carbeing a lemon.


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However the report costs $50. So you could riskit, and buy the car without the report.

Owning a sound car is better than having no

car, which is better than owning a lemon.


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Car Buyer’sNetwork Rep: good bad none

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Car Buyer s Network


Lemon

Report

Inspect Buy

U

l ¬l0.5 0.5

g b n

l i 0.2 0.8 0¬l i 0.9 0.1 0l ¬i 0 0 1¬l ¬i 0 0 1

Rep: good,bad,none

b l -600 b ¬l 1000

¬b l -300

¬b¬l -300

Utility

-50 ifinspect

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Evaluate Last Decision: Buy (1)

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y ( )

EU(B|I,R) = ΣL P(L|I,R,B)U(L,B) The probability of the remaining variables in the Utility function, times

the utility function. Note P(L|I,R,B) = P(L|I,R), as B is a decisionvariable that does not influence L.

I = i, R = g:

P(L|i,g): use variable elimination. Query variable L is only remainingvariable, so we only need to normalize (no summations).

P(L,i,g) = P(L)P(g|L,i)HENCE: P(L|i,g) = normalized [P(l)P(g|l,i),P(¬l)P(g|¬l,i)= [0.5*.2, 0.5*0.9] = [.18, .82]

EU(buy) = P(l|i,g)U(buy,l) + P(¬l)P(¬l|i,g) U(buy,¬l)-50= .18*-600 + .82*1000 – 50 = 662

EU(¬buy) = P(l|i, g) U(¬buy,l) + P(¬l|i, g) U(¬buy,¬l) – 50= .18*-300 + .82*-300 -50 = -350

So optimal δ Buy (i,g) = buy


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y ( )

I = ¬i, R = n

P(L,¬i,n) = P(L)P(n|L,¬i)

P(L|¬i,n) = normalized [P(l)P(n|l,¬i),P(¬l)P(n|¬l,¬i)

= [0.5*1, 0.5*1] = [.5,.5]

EU(buy) = P(l|¬i,n) U(l,buy) + P(¬l|¬i,n) U(¬l,buy)

= .5*-600 + .5*1000 = 200 (no inspection cost)

EU(¬buy) = P(l|¬i, n) U(l,¬buy) + P(¬l|¬i, n) U(¬l,¬buy)

= .5*-300 +.5*-300 = -300 So optimal δ Buy (¬i,n) = buy

Overall optimal policy for Buy is: δ Buy (i,g) = buy ; δ Buy (i,b) = ¬buy ; δ Buy (¬i,n) = buy

Note: we don’t bother computing policy for (i,¬n), (¬i, g), or (¬i, b), since

these occur with probability 0


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Evaluate First Decision: Inspect

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p

EU(I) = ΣL,R P(L,R|I) U(L, Buy (I,R))

where P(R,L|I) = P(R|L,I)P(L|I)

EU(i) = .1*-600 + .4*-300 + .45*1000 + .05*-300 - 50

= 237.5 – 50 = 187.5

EU(¬i) = P(l|¬i, n) U(l,buy) + P(¬l|¬i, n) U(¬l,buy)

= .5*-600 + .5*1000 = 200

So optimal δ Inspect (¬i) = buy

P(R,L |i)

y

U( L,y

)

g,l 0.2*.5 = .1 buy -600 - 50 = -650

b,l 0.8*.5 = .4 ¬buy -300 - 50 = -350

g,¬l 0.9*.5 = .45 buy 1000 - 50 = 950

b,¬l 0.1*.5 = .05 ¬buy -300 - 50 = -350


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Value of Information

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So optimal policy is: don’t inspect, buy the car EU = 200

Notice that the EU of inspecting the car, then buying it iff

you get a good report, is 237.5 less the cost of theinspection (50). So inspection not worth the improvement inEU.

But suppose inspection cost $25: then it would be worth it

(EU = 237.5 – 25 = 212.5 > EU(¬i)) The exp e c ted va lue o f in fo rm a t io n associated with

inspection is 37.5 (it improves expected utility by thisamount ignoring cost of inspection). How? Gives

opportunity to change decision (¬buy if bad).

You should be willing to pay up to $37.5 for the report


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lectures 08 decision making

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