ketidakpastian (uncertainty) yeni herdiyeni – “uncertainty is defined as the lack of the exact...
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KETIDAKPASTIAN (UNCERTAINTY)
Yeni Herdiyeni – http://ilkom.fmipa.ipb.ac.id/yeni
“Uncertainty is defined as the lack of the exact knowledge that would enable us to reach a perfectly reliable conclusion”
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Topik
Pengertian Ketidakpastian (Uncertainty) Konsep Dasar Peluang (Probability) Faktor kepastian (Certainty Factor) Penalaran Bayes (Bayesian Reasoning) Fuzzy Neural Network Genetic Algorithm
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Ketidakpastian
Hujan atau Tidak ya….???
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Expert System in Nursing
For example, a nursing diagnosis may require to know if a patient had suffered from a certain allergy?
The patient may not know or remember the answer to this question. Therefore, the patient information is unavailable to ensure a correct diagnosis.
This means that decisions will often be based on incomplete or uncertain data. Clearly, this may result in uncertain conclusions.
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Ide
Konsep Dasar Peluang (Probability)
Faktor kepastian (Certainty Factor)
Penalaran Bayes (Bayesian Reasoning)
Fuzzy Neural Network Genetic Algorithm
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Aplikasi
Financial Market : Stock Market Games : Gambling Weather Forecasts Risk Management dll
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Studi Kasus
PASS : An Expert System with Certainty Factors for Predicting Student Success
Variabel : Jenis Kelamin, spesialisasi, Peringkat
Peringkat: Moderate : >=10 and <12.5 Good : >=12.5 and <15.5 Very Good: >=15.5 and <18.5 Excellent: >=18.5 and <=20
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Ketidakpastian
Informasi seringkali tidak lengkap, tidak konsisten, tidak pasti (uncertain)
Weak Implication Imprecise language : ambigu, imprecise Unknown data Menggabungkan perbendaan pandangan
dari pakar
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9
Sources of Uncertain KnowledgeImpreciselanguage
Term
AlwaysVery oftenUsuallyOftenGenerallyFrequentlyRather oftenAbout as often as notNow and thenSometimesOccasionallyOnce in a whileNot oftenUsually notSeldomHardly everVery seldomRarelyAlmost neverNever
Mean value
99888578787365502020201513101076530
Term
AlwaysVery oftenUsuallyOften
GenerallyFrequentlyRather often
About as often as notNow and thenSometimesOccasionallyOnce in a whileNot oftenUsually notSeldomHardly everVery seldomRarelyAlmost neverNever
Mean value
10087797474727250342928221616987520
Milton Hakel (1968)Ray Simpson (1944)
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Uncertainty
In evidence: 'if pressure gauge is high then liquid is
boiling'
Electrical components are notoriously faulty, the pressure gauge may actually be stuck
In inferring conclusion: 'if patient has a sore throat then patient has
tonsillitis'
A doctor may infer such a conclusion but it would not be an absolute, binary, one.
Vagueness of language: 'if the car is a Porsch then it is fast'.
What do we mean when using the term fast?
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Representasi Ketidakpastian
Certainty Theory Probabilistic Theory of evidence (Bayes Theorm) Fuzzy logic Neural Network GA
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Certainty Theory
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Certainty Theory
A certainty factor (cf ), a number to measure the expert’s belief.
Maximum value of the certainty factor is +1.0 (definitely true) and minimum -1.0 (definitely false).
Two aspects: Certainty in the Evidence
The evidence can have a certainty factor attached
Certainty in Rule Note CF values are not probabilities, but informal
measures of confidence
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Certainty Factors
Term Certainty Factor
Definitely not -1.0
Almost certainly not -0.8
Probably not -0.6
Maybe not -0.4
Unknown -0.2 to +0.2
Maybe +0.4
Probably +0.6
Almost certainly +0.8
Definitely +1.0
Uncertain term and their intepretation
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Expert Systems with Certainty Theory
In expert systems with certainty factors, the knowledge base consists of a set of rules that have the following syntax:
IF <evidence>THEN <hypothesis> {cf }
Where cf represents belief in hypothesis H given that evidence E has occurred.
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The certainty factor assigned by a rule is propagated through the reasoning chain
Now belief in hypothesis H given that evidence E has occurred, is: cf (H,E) = cf (E) * cf(Rule)
For example:IF sky is clearTHEN the forecast is sunny {cf 0.8}
and current certainty factor of “sky is clear” is 0.5 cf (H,E) = 0.5 * 0.8 = 0.4
Example
The certainty that the forcast is sunny = 0.4
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Multiple Antecedents
So if a certainty is attached to the evidence What happens when we have rules with
more than one piece of evidence? With conjunctions (i.e. and)
Use the minimum cf of evidence With disjunctions (i.e. or)
Use the maximum cf of evidence
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Conjunctive Antecedents - Example Conjunctive Rules:
cf(H, E1 and E2 and …Ei) = min{c(E1 , E2 , …Ei )} *cf(Rule)
IF there are dark clouds E1
AND the wind is stronger E2
THEN it will rain {cf 0.8}
So assume that cf(E1) = 0.5 and cf(E2) = 0.9, then
cf(H, E) = min{0.5, 0.9} * 0.8 = 0.4
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Disjunctive Antecedents - Example Disjunctive Rules:
cf(H, E1 or E2 or …Ei) = max{cf(E1 , E2 , …Ei )} * cf(Rule)
IF there are dark clouds E1
OR the wind is stronger E2
THEN it will rain {cf 0.8}
Again assume that cf(E1) = 0.5 and cf(E2) = 0.9, then
cf(H, E) = max{0.5, 0.9} * 0.8 = 0.72
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What if two pieces of evidence leads to the same conclusion?
Common sense suggests that, if two pieces of evidence from different sources support the same hypothesis then confidence in this hypothesis should increase more than if only one piece of evidence had been obtained.
Similarly Concluded Rules
Rule 1: IF A is XTHEN C is Z {cf 0.8}
Rule 2: IF B is YTHEN C is Z {cf 0.6}
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Similarly Concluded Rules - Example
Two rules can lead to same conclusion:
IF weatherperson predicts rain E1
THEN it will rain{cf 1 0.7}
IF farmer predicts rain E2
THEN it will rain{cf 2 0.9}
Assume cf(E1) = 1.0 and that cf(E2 ) = 1.0
cf 1(H1, E1) = cf(E1) * cf(Rule1)
cf 1(H1, E1) = 1.0 * 0.7 = 0.7
cf 2 (H2, E2) = cf(E2) * cf(Rule2)
cf 2(H2, E2) = 1.0 * 0.9 = 0.9
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Similarly Concluded Rules
If we obtain supporting evidence for hypothesis from two sources, then should feel more confident in conclusion
So:
cf combine(cf 1, cf 2) = cf 1 + cf 2 * (1 - cf 1)
= 0.7 + 0.9 * ( 1 – 0.7)
= 0.97
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Certainty Theory
So certainty theory allows us to reason with uncertainty in: evidence inferring conclusion – i.e. the hypothesis
But what about vagueness of language: 'if the car is a Porsch then it is fast'.
For this we use Fuzzy Logic
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Probability
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21st September 2006Bogdan L. Vrusias © 2006
25
Basic Probability Theory
The concept of probability has a long history that goes back thousands of years when words like “probably”, “likely”, “maybe”, “perhaps” and “possibly” were introduced into spoken languages. However, the mathematical theory of probability was formulated only in the 17th century.
The probability of an event is the proportion of cases in which the event occurs. Probability can also be defined as a scientific measure of chance.
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26
Basic Probability Theory
Probability can be expressed mathematically as a numerical index with a range between zero (an absolute impossibility) to unity (an absolute certainty).
Most events have a probability index strictly between 0 and 1, which means that each event has at least two possible outcomes: favourable outcome or success, and unfavourable outcome or failure.
outcomes possible of number the
successesof number thesuccessP
outcomes possible of number the
failures of number thefailureP
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Basic Probability Theory
If s is the number of times success can occur, and f is the number of times failure can occur, then
and p + q = 1
If we throw a coin, the probability of getting a head will be equal to the probability of getting a tail. In a single throw, s = f = 1, and therefore the probability of getting a head (or a tail) is 0.5.
fs
spsuccessP
fs
fqfailureP
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The Axioms of Probability
0 <= P(A) <= 1 P(True) = 1 P(False) = 0 P(A or B) = P(A) + P(B) - P(A and B)
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Theorems from the Axioms
0 <= P(A) <= 1, P(True) = 1, P(False) = 0 P(A or B) = P(A) + P(B) - P(A and B)
From these we can prove:P(not A) = P(~A) = 1-P(A)
A
B
P(A or B)
BP(A and B)
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Copyright © Andrew W. Moore
Another important theorem
0 <= P(A) <= 1, P(True) = 1, P(False) = 0 P(A or B) = P(A) + P(B) - P(A and B)
From these we can prove:P(A) = P(A ^ B) + P(A ^ ~B)
How?
A
B
P(A or B)
BP(A and B)
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Conditional Probability
Let A be an event in the world and B be another event. Suppose that events A and B are not mutually exclusive,
but occur conditionally on the occurrence of the other. The probability that event A will occur if event B occurs is
called the conditional probability. Conditional probability is denoted mathematically as p(A|B)
in which the vertical bar represents "given" and the complete probability expression is interpreted as “Conditional probability of event A occurring given that event B
has occurred”.
occur can B times of number the
occur can B and A times of number theBAp
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Copyright © Andrew W. Moore
Conditional Probability
P(A|B) = Fraction of worlds in which B is true that also have A true
F
H
H = “Have a headache”F = “Coming down with Flu”
P(H) = 1/10P(F) = 1/40P(H|F) = 1/2
“Headaches are rare and flu is rarer, but if you’re coming down with ‘flu there’s a 50-50 chance you’ll have a headache.”
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Copyright © Andrew W. Moore
Conditional Probability
F
H
H = “Have a headache”F = “Coming down with Flu”
P(H) = 1/10P(F) = 1/40P(H|F) = 1/2
P(H|F) = Fraction of flu-inflicted worlds in which you have a headache
= #worlds with flu and headache ------------------------------------ #worlds with flu
= Area of “H and F” region ------------------------------ Area of “F” region
= P(H ^ F) ----------- P(F)
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Copyright © Andrew W. Moore
Definition of Conditional Probability
P(A ^ B) P(A|B) = ----------- P(B)
Corollary: The Chain Rule
P(A ^ B) = P(A|B) P(B)
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Copyright © Andrew W. Moore
Probabilistic Inference
F
H
H = “Have a headache”F = “Coming down with Flu”
P(H) = 1/10P(F) = 1/40P(H|F) = 1/2
One day you wake up with a headache. You think: “Drat! 50% of flus are associated with headaches so I must have a 50-50 chance of coming down with flu”
Is this reasoning good?
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Copyright © Andrew W. Moore
Probabilistic Inference
F
H
H = “Have a headache”F = “Coming down with Flu”
P(H) = 1/10P(F) = 1/40P(H|F) = 1/2
P(F ^ H) = …
P(F|H) = …
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21st September 2006Bogdan L. Vrusias © 2006
37
Conditional Probability
The number of times A and B can occur, or the probability that both A and B will occur, is called the joint probability of A and B. It is represented mathematically as p(AB). The number of ways B can occur is the probability of B, p(B), and thus
Similarly, the conditional probability of event B occurring given that event A has occurred equals
Bp
BApBAp
Ap
ABpABp
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Conditional Probability
Hence
And
Substituting the last equation into the equation
yields the Bayesian rule:
ApABpABp
ApABpBAp
Bp
BApBAp
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Copyright © Andrew W. Moore
What we just did…
P(A ^ B) P(A|B) P(B)
P(B|A) = ----------- = ---------------
P(A) P(A)
This is Bayes RuleBayes, Thomas (1763) An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53:370-418
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Copyright © Andrew W. Moore
Another way to understand the intuition
Thanks to Jahanzeb Sherwani for contributing this explanation:
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Papers
Uncertainty Management in Expert System
PASS : An Expert System with Certainty Factors for Predicting Student Success
FuzzyCLIPS
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Next Week
Bayesian Inference Fuzzy