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THE HONG KONG UNIVERSITY OF SCIENCE & TECHNOLOGY CSIT 5220: Reasoning and Decision under Uncertainty L09: Graphical Models for Decision Problems Nevin L. Zhang Room 3504, phone: 2358-7015, Email: lzhang@cs.ust.hk Home pagelzhang@cs.ust.hkHome page

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CSIT 5220 L10: Graphical Models for Decision Problems l Introduction l Extending BN to Include a Single Decision l Fundamentals of Rational Decision Making l Decision Trees l Influence Diagrams l Solving influence Diagrams l Value of information Page 2

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CSIT 5220 Probabilistic Reasoning and Decision l Method 1: Two-stage n In a BN, calculate posterior probabilities n Use the posteriors to make decisions l Method 2 n Combine the two stages n Extend BN to include decisions Better reveal structure of decision problem Compute optimal decisions directly from model l Reasoning: Jensen & Nielsen, Sections 9.1-9.4, 10.2, 11.1 Page 3

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CSIT 5220 L10: Graphical Models for Decision Problems l Extending BN to Include a Single Decision l Fundamentals of Rational Decision Making l Decision Trees l Influence Diagrams l Solving influence Diagrams l Value of information Page 4

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CSIT 5220 Poker l From Lecture 04 Page 5 l Extend the model so that I can calculate the probability that my hand is better than the opponent’s hand l MH: My Hand l BH: Best Hand

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CSIT 5220 Fold or Call Page 6

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CSIT 5220 Fold or Call l Information that I have: FC, SC, MH Page 7

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CSIT 5220 Modeling One Action l Start with a BN l Add the decision node and utility nodes n What information we have when making the decision n What chance and utility variables will the decision influence Page 8

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CSIT 5220 Including More Decisions l Things become a bit more complicated. l Will see later. Page 9

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CSIT 5220 L10: Graphical Models for Decision Problems l Extending BN to Include Decisions l Fundamentals of Rational Decision Making l Decision Trees l Influence Diagrams l Solving influence Diagrams l Value of information Page 10

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CSIT 5220 Decision Theory l Normative decision theory n How people should decide. (Rational agent) l Descriptive decision theory n How people actually decide. Page 11

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CSIT 5220 Normative Decision Theory Page 12

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CSIT 5220 Are you rational? l Lottery A: [$1mill] l Lottery B: 0.5[$2mill] + 0.5[$0mill] l Which one do you choose? l Most people would choose A U(1) > 0.5 U(2) + 0.5 U(0) l Most people are risk-averse, with concave utility function Page 13

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CSIT 5220 Are your rational? l Suppose that you are $2mill in debt Page 14 l Lottery A: [$1mill] l Lottery B: 0.5[$2mill] + 0.5[$0mill] l Which one do you choose? l Probably B U(1) < 0.5 U(2) + 0.5 U(0) l You are being risk-seeking, with convex utility function

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CSIT 5220 Utilities without Money Page 15

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CSIT 5220 Utilities without Money Page 16

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CSIT 5220 Marks as Utilities Page 17

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CSIT 5220 Other Considerations l 2 is passing grade l If fail, can retake and hopefully get a better grade in transcript n In this case, 2 is the worst! Page 18

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CSIT 5220 L10: Graphical Models for Decision Problems l Extending BN to Include Decisions l Fundamentals of Rational Decision Making l Decision Trees l Influence Diagrams l Solving influence Diagrams l Value of information Page 19

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CSIT 5220 Decision Trees l Classical way to represent decision problems with multiple decisions l Explicitly show all possible sequences of decisions and observations. l Example: Oil Wildcatter Page 20 A wildcatter is a person who drills wildcat wells, which are oil wells drilled in areas not known to be oil fields.drills wildcat wellsoil wellsoil fields Test on Seismic structure

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CSIT 5220 Decision Tree for Oil Wildcatter Page 21

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CSIT 5220 Decision Trees l Decision nodes: Rectangles l Chance nodes: ellipses l Utility values: at leaves, some times inside diamonds l To be read from root to leaves n Branches from a decision node: possible actions n Branches from a chance node: possible outcomes and probs n A decision node follows a chance node: The chance node is observed before the decision is made n No-forgetting Decision-maker remembers all the labels from root to a decision node l Game between decision maker and nature Page 22

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CSIT 5220 Solution to a Decision Tree l Strategy: Which decision node to pick at each decision node Page 23

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CSIT 5220 Solution to a Decision Tree l Optimal Strategy: The strategy with the highest expected utility Page 24

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CSIT 5220 Solving Decision Trees Page 25

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CSIT 5220 Example Page 26 77.59

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CSIT 5220 Page 27

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CSIT 5220 L10: Graphical Models for Decision Problems l Extending BN to Include Decisions l Fundamentals of Rational Decision Making l Decision Trees l Influence Diagrams l Solving influence Diagrams l Value of information Page 28

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CSIT 5220 Extending BN to Including one Decision Page 29 l Start with a BN l Add the decision node and utility nodes n What information we have when making the decision n What chance and utility variables will the decision influence l To include multiple decision nodes, n Need to consider the interactions among the decisions

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CSIT 5220 Including Multiple Decisions l Two more decisions n MFC: my first change n MSC: my second change Page 30

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CSIT 5220 Representing the Decision Sequence l First representation n All nodes observed before a decision are parents of that decision. n Information arcs. Page 31 l Assume that the decision maker doesn’t forget, then some links are redundant.

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CSIT 5220 Representing the Decision Sequence l No-forgetting allows a more concise representation n Keep directed path going through all the decision node: Order of decision. n Arrows into a decision node only from those nodes observed immediately before that decision. n Implicit parents: parents of earlier decisions Page 32

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CSIT 5220 Influence Diagram l A DAG with three types of nodes n Chance nodes, decision nodes, and utility nodes l There is a directed path containing all the decision nodes. l The utility nodes have no children. l Each chance node is associated with the conditional distribution given its parents. l Each utility node is associated with a utility function, a real-valued function of its parents. Page 33

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CSIT 5220 Influence Diagram Page 34

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CSIT 5220 l An influence diagram for the oil wildcatter problem n Decision: T: test = {y, n}; D: drill={y, n} n Utility: C: cost of test ; V: Benefit of drilling n Chance: O: Oil ={dry, wet, soaking} R: seismic structure {no-structure, open-structure, closed-structure, no-result} Influence Diagram Page 35

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CSIT 5220 L10: Graphical Models for Decision Problems l Extending BN to Include Decisions l Fundamentals of Rational Decision Making l Decision Trees l Influence Diagrams l Solving influence Diagrams l Value of information Page 36

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CSIT 5220 Strategy (Policy) l A policy specifies what to do for each decision l It is a function of observed variables Page 37 l Different policies lead to different expected utility l Optimal policy: the Policy that yields the maximum expected utility. l How to find the optimal policy?

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CSIT 5220 Finding Optimal Policy l First idea: n Convert to decision tree and solve it l How to convert influence diagram into decision tree 1. Draw tree nRoot: the thing that happens first nChildren of root: the thing that happens next n…n… 2. Figure out numerical information Page 38

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CSIT 5220 l Order of events l Tree structure l Numerical info n Prob for branches from chance node n Utility for leaves

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CSIT 5220 A Side Note l Two decision trees for Oil Wildcatter n First directly from problem specification. Asymmetric n Second from influence diagram Symmetric l Pro of ID: compact l Con of ID: cannot represent assymetry n Need to introduce artificial state R = no-result

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CSIT 5220 Finding Optimal Policy l First idea: n Convert to decision tree and solve it l Exponential still! l Next: n Variable Elimination Algorithm for solving influence diagrams n Note n BN inference: All orderings give correct result, but might have different complexity n ID: Must use “strong elimination orderings”. Page 41

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CSIT 5220 Temporal Order and Decisions andd Observations l Notations n Decision nodes have a temporal order: D 1, D 2, …, D n n T 0 : Set of chance nodes observed prior to any decision n T i : Set of chance nodes observed after D i is taken and before D i+1 is taken l Oil Wildcatter n D 1 = T; D 2 = D n T 0 = {}; T 1 = {R}; T 2 ={O} l Partial temporal order n T 0, D 1, T 1, D 2, T 2, …., D n, T n n Oil Wildcatter: T, R, D, O Page 42

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CSIT 5220 Temporal Order l T 0 ={}, T 1 ={T}, T 2 ={A, B, C} l Partial temporal ordering n D 1, T, D 2. {A, B, C} n No ordering among A, B, C Page 43

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CSIT 5220 Strong Elimination Ordering l Partial temporal order n T 0, D 1, T 1, D 2, T 2, …., D n, T n l Strong elimination orders n First eliminate variables in T n n Then eliminate D n n Then eliminate variables in T n-1 n Then eliminate D n-1 n ….. l Oil Wildcatter n Temporal order: T, R, D, O n Strong elimination ordering O, D, R, T Page 44

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CSIT 5220 Strong Elimination Ordering l T 0 ={}, T 1 ={T}, T 2 ={A, B, C} l Partial temporal ordering n D 1, T, D 2. {A, B, C} n No ordering among A, B, C l Strong elimination orderings n A, B, C, D 2, T, D1 n B, C, A, D 2, T, D 1 n C, A, B, D 2, T, D 1 n …. Page 45

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CSIT 5220 Variable Elimination l Two set of potentials (factors): l Eliminate decision and chance nodes one by one according to a strong elimination ordering. l When eliminate variable X Page 46

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CSIT 5220 Variable Elimination on Oil Wildcatter l Strong Elimination Ordering: O, D, R, T Page 47

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CSIT 5220 l Eliminate: O Page 48 Variable Elimination on Oil Wildcatter

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CSIT 5220 Page 49

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CSIT 5220 Page 50

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CSIT 5220 Potentials after Eliminating O Page 51

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CSIT 5220 Potentials after Eliminating O Page 52

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CSIT 5220 Eliminating D l No probability potential involves D Page 53 l Optimal decision for D

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CSIT 5220 Potentials after Eliminating D Page 54

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CSIT 5220 Eliminating R Page 55

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CSIT 5220 Potentials after Eliminating R Page 56

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CSIT 5220 Eliminating T Page 57 l Optimal decision for T l Results same as those by decision tree

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CSIT 5220 Solving Influence Diagram Using Netica

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CSIT 5220 Solving Influence Diagram Using Netica l Netica cannot handle multiple utility l So, combine U and V

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CSIT 5220 Solving Influence Diagram Using Netica

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CSIT 5220 l Or, Get optimal action by trying each actions

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CSIT 5220 L10: Graphical Models for Decision Problems l Extending BN to Include Decisions l Fundamentals of Rational Decision Making l Decision Trees l Influence Diagrams l Solving influence Diagrams l Value of information Page 62

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CSIT 5220 Two types of Decisions l Action decisions n Result in significant state change of variables of interest n Example: D: Drill or not to drill l Test decisions n Look for more evidence n Example: T: Test of Seismic structure Page 63

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CSIT 5220 Two types of Decisions l Typical scenario n Need to make one decision n Want to get more information before making the decision n Question Is it worthwhile to perform a particular test? Which test to choose if multiple tests are available? Page 64

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CSIT 5220 Value of Information l What is the value of a test? n Create two influence diagrams n Solve both n Compare their values l Example: Oil wildcatter n Is it worthwhile to perform the seismic test? n ID1: without the test n ID2: with the test Page 65

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CSIT 5220 Value of Information l Expected utility of ID2 n U(ID2) = 22.55 l What is the expected utility of ID1? Page 66

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CSIT 5220 Expected Utility of ID1 l Temporal ordering: D, O l Elimination ordering: O, D l Eliminate O:

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CSIT 5220 Expected Utility of ID1 l Potentials after eliminating O l Eliminate D l Expected utility of ID1 n U(ID1) = 20 Page 68

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CSIT 5220 Value of Information l Difference in expected utility n U(ID2) – U(ID1) = 22.55 – 20 = 2.55 n The expected value of the seismic test is 2.55 n The test is worthwhile Page 69

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CSIT 5220 Value of Information l If there are multiple tests n T1, T2, T3, … n Compute the value of each test, pick the best one n If the value of the best is positive, Pick the test among remain tests n Stop when value of the selected test is not positive Page 70

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