1. Reasoning Under Uncertainty Artificial Intelligence CMSC 25000 February 19, 2008

2. Agenda Motivation –Reasoning with uncertainty Medical Informatics Probability and Bayes’ Rule –Bayesian Networks –Noisy-Or Decision Trees and Rationality Conclusions

3. Uncertainty Search and Planning Agents –Assume fully observable, deterministic, static Real World: –Probabilities capture “Ignorance & Laziness” Lack relevant facts, conditions Failure to enumerate all conditions, exceptions –Partially observable, stochastic, extremely complex –Can't be sure of success, agent will maximize –Bayesian (subjective) probabilities relate to knowledge

4. Motivation Uncertainty in medical diagnosis –Diseases produce symptoms –In diagnosis, observed symptoms => disease ID –Uncertainties Symptoms may not occur Symptoms may not be reported Diagnostic tests not perfect –False positive, false negative How do we estimate confidence?

5. Motivation II Uncertainty in medical decision-making –Physicians, patients must decide on treatments –Treatments may not be successful –Treatments may have unpleasant side effects Choosing treatments –Weigh risks of adverse outcomes People are BAD at reasoning intuitively about probabilities –Provide systematic analysis

6. Probability Basics The sample space: – A set Ω ={ω1, ω2, ω3,… ωn} E.g 6 possible rolls of die; ωi is a sample point/atomic event Probability space/model is a sample space with an assignment P(ω) for every ω in Ω s.t. 0<= P(ω)<=1; Σ ωP(ω) = 1 –E.g. P(die roll < 4)=1/6+1/6+1/6=1/2

7. Random Variables A random variable is a function from sample points to a range (e.g. reals, bools) E.g. Odd(1) = true P induces a probability distribution for any r.v X: –P(X=xi) = Σ{ω:X(ω)=xi}P(ω) –E.g. P(Odd=true)=1/6+1/6+1/6=1/2 Proposition is event (set of sample pts) s.t. proposition is true: e.g. event a= A(ω)=true

8. Why probabilities? Definitions imply that logically related events have related probabilities In AI applications, sample points are defined by set of random variables –Random vars: boolean, discrete, continuous

9. Prior Probabilities Prior probabilities: belief prior to evidence –E.g. P(cavity=t)=0.2; P(weather=sunny)=0.6 –Distribution gives values for all assignments Joint distribution on set of r.v.s gives probability on every atomic event of r.v.s –E.g. P(weather,cavity)=4x2 matrix of values Every question about a domain can be answered with joint b/c every event is a sum of sample pts

10. Conditional Probabilities Conditional (posterior) probabilities –E.g. P(cavity|toothache) = 0.8, given only that –P(cavity|toothache)=2 elt vector of 2 elt vectors Can add new evidence, possibly irrelevant P(a|b) = P(a^b)/P(b) where P(b) ≠0 Also, P(a^b)=P(a|b)P(b)=P(b|a)P(a) –Product rule generalizes to chaining

11. Inference By Enumeration

12. Inference by Enumeration

14. Independence

15. Conditional Independence

16. Conditional Independence II

17. Probabilities Model Uncertainty The World - Features –Random variables –Feature values States of the world –Assignments of values to variables –Exponential in # of variables – possible states

18. Probabilities of World States : Joint probability of assignments –States are distinct and exhaustive Typically care about SUBSET of assignments –aka “Circumstance” –Exponential in # of don’t cares

19. A Simpler World 2^n world states = Maximum entropy –Know nothing about the world Many variables independent –P(strep,ebola) = P(strep)P(ebola) Conditionally independent –Depend on same factors but not on each other –P(fever,cough|flu) = P(fever|flu)P(cough|flu)

20. Probabilistic Diagnosis Question: –How likely is a patient to have a disease if they have the symptoms? Probabilistic Model: Bayes’ Rule P(D|S) = P(S|D)P(D)/P(S) –Where P(S|D) : Probability of symptom given disease P(D): Prior probability of having disease P(S): Prior probability of having symptom

21. Diagnosis Consider Meningitis: –Disease: Meningitis: m –Symptom: Stiff neck: s –P(s|m) = 0.5 –P(m) =0.0001 –P(s) = 0.1 –How likely is it that someone with a stiff neck actually has meningitis?

22. Modeling (In)dependence Simple, graphical notation for conditional independence; compact spec of joint Bayesian network –Nodes = Variables –Directed acyclic graph: link ~ directly influences –Arcs = Child depends on parent(s) No arcs = independent (0 incoming: only a priori) Parents of X = For each X need

23. Example I

24. Simple Bayesian Network MCBN1 ABCDE A = only a priori B depends on A C depends on A D depends on B,C E depends on C Need: P(A) P(B|A) P(C|A) P(D|B,C) P(E|C) Truth table 2 2*2 2*2*2 2*2

25. Simplifying with Noisy-OR How many computations? –p = # parents; k = # values for variable –(k-1)k^p –Very expensive! 10 binary parents=2^10=1024 Reduce computation by simplifying model –Treat each parent as possible independent cause –Only 11 computations 10 causal probabilities + “leak” probability –“Some other cause”

26. Noisy-OR Example AB Pn(b|a) = 1-(1-ca)(1-L) Pn(b|a) = (1-ca)(1-L) Pn(b|a) = 1-(1 -L) = L = 0.5 Pn(b|a) = (1-L) P(B|A)b aaaa 0.6 0.4 0.5 Pn(b|a) = 1-(1-ca)(1-L)=0.6 (1-ca)(1-L)=0.4 (1-ca) =0.4/(1-L) =0.4/0.5=0.8 ca = 0.2

27. Noisy-OR Example II ABC Full model: P(c|ab)P(c|ab)P(c|ab)P(c|ab) & neg Noisy-Or: ca, cb, L Pn(c|ab) = 1-(1-ca)(1-cb)(1-L) Pn(c|ab) = 1-(1-cb)(1-L) Pn(c|ab) = 1-(1-ca)(1-L) Pn(c|ab) = 1-(1-L) Assume: P(a)=0.1 P(b)=0.05 Pn(c|ab)=0.3 ca= 0.5 Pn(c|b) = 0.7 = L = 0.3 Pn(c|b)=Pn(c|ab)P(a)+Pn(c|ab)P(a) 1-0.7=(1-ca)(1-cb)(1-L)0.1+(1-cb)(1-L)0.9 0.3=0.5(1-cb)0.07+(1-cb)0.7*0.9 =0.035(1-cb)+0.63(1-cb)=0.665(1-cb) 0.55=cb

28. Graph Models Bipartite graphs –E.g. medical reasoning –Generally, diseases cause symptom (not reverse) d1d2d3d4s1s2s3s4s5s6

29. Topologies Generally more complex –Polytree: One path between any two nodes General Bayes Nets –Graphs with undirected cycles No directed cycles - can’t be own cause Issue: Automatic net acquisition –Update probabilities by observing data –Learn topology: use statistical evidence of indep, heuristic search to find most probable structure

30. Holmes Example (Pearl) Holmes is worried that his house will be burgled. For the time period of interest, there is a 10^-4 a priori chance of this happening, and Holmes has installed a burglar alarm to try to forestall this event. The alarm is 95% reliable in sounding when a burglary happens, but also has a false positive rate of 1%. Holmes’ neighbor, Watson, is 90% sure to call Holmes at his office if the alarm sounds, but he is also a bit of a practical joker and, knowing Holmes’ concern, might (30%) call even if the alarm is silent. Holmes’ other neighbor Mrs. Gibbons is a well-known lush and often befuddled, but Holmes believes that she is four times more likely to call him if there is an alarm than not.

31. Holmes Example: Model There a four binary random variables: B: whether Holmes’ house has been burgled A: whether his alarm sounded W: whether Watson called G: whether Gibbons called BAWG

32. Holmes Example: Tables B = #t B=#f 0.0001 0.9999 A=#t A=#f B #t #f 0.95 0.05 0.01 0.99 W=#t W=#fA #t #f 0.90 0.10 0.30 0.70 G=#t G=#fA #t #f 0.40 0.60 0.10 0.90

33. Decision Making Design model of rational decision making –Maximize expected value among alternatives Uncertainty from –Outcomes of actions –Choices taken To maximize outcome –Select maximum over choices –Weighted average value of chance outcomes

34. Gangrene Example MedicineAmputate foot Live 0.99 Die 0.01 8500 Die 0.05 0 Full Recovery 0.7 1000 Worse 0.25 MedicineAmputate leg Die 0.4 0 Live 0.6 995 Die 0.02 0 Live 0.98 700

35. Decision Tree Issues Problem 1: Tree size –k activities : 2^k orders Solution 1: Hill-climbing –Choose best apparent choice after one step Use entropy reduction Problem 2: Utility values –Difficult to estimate, Sensitivity, Duration Change value depending on phrasing of question Solution 2c: Model effect of outcome over lifetime

36. Conclusion Reasoning with uncertainty –Many real systems uncertain - e.g. medical diagnosis Bayes’ Nets –Model (in)dependence relations in reasoning –Noisy-OR simplifies model/computation Assumes causes independent Decision Trees –Model rational decision making Maximize outcome: Max choice, average outcomes

37. Holmes Example (Pearl) Holmes is worried that his house will be burgled. For the time period of interest, there is a 10^-4 a priori chance of this happening, and Holmes has installed a burglar alarm to try to forestall this event. The alarm is 95% reliable in sounding when a burglary happens, but also has a false positive rate of 1%. Holmes’ neighbor, Watson, is 90% sure to call Holmes at his office if the alarm sounds, but he is also a bit of a practical joker and, knowing Holmes’ concern, might (30%) call even if the alarm is silent. Holmes’ other neighbor Mrs. Gibbons is a well-known lush and often befuddled, but Holmes believes that she is four times more likely to call him if there is an alarm than not.

38. Holmes Example: Model There a four binary random variables: B: whether Holmes’ house has been burgled A: whether his alarm sounded W: whether Watson called G: whether Gibbons called BAWG

39. Holmes Example: Tables B = #t B=#f 0.0001 0.9999 A=#t A=#f B #t #f 0.95 0.05 0.01 0.99 W=#t W=#fA #t #f 0.90 0.10 0.30 0.70 G=#t G=#fA #t #f 0.40 0.60 0.10 0.90