Download PDF by Kurt Weichselberger: A Methodology for Uncertainty in Knowledge-Based Systems

By Kurt Weichselberger

ISBN-10: 3540523367

ISBN-13: 9783540523369

In this publication the resultant use of chance concept is proposed for dealing with uncertainty in specialist platforms. it really is proven that equipment violating this advice could have risky results (e.g., the Dempster-Shafer rule and the strategy utilized in MYCIN). the need of a few specifications for an accurate combining of doubtful details in professional structures is proven and appropriate ideas are supplied. the prospect is considered that period estimates are given rather than distinct information regarding chances. For combining details containing period estimates principles are supplied that are helpful in lots of cases.

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Example text

21) is not to be seen. Furthermore it is noteworthy that in a way quite similar to that which leads to the Dempster-Shafer rule, another type of combination rule may be derived which does not have the tendency to be too optimistic, but the tendency to be too pessimistic. In this case the basic probability number m l ( B i l ) ' . . UBil. Since this subset of O can only be ¢ if each Bij = ¢, the necessity of conditioning does not exist if this procedure is applied. 21) but a modification. 21): If frmn one source of information it is concluded that Bil occurs and from the other one it is concluded that Bi2 occurs, one should only conclude, that Bil or Bi2 occur.

6 m(EiUEj) = 0 . 2 The same calculation could be carried out for the estimates b) and d). 18) and with the interpretation of Li as belief functions and Ui as upper probabilities. [] Through this definition the requirements of Shafer's theory of belief functions, concerning the concept of k - P R I s - which in itself is not a topic of Shafer's - are characterized. 2): Be 0 = {El . . , Ek} k j~ILj - Li+~ti~2+;~i3' + "'" +;ti~n-I Be] (TEl) = 1 - U i = where k j I-I E2 = Y, Y~ m(EJl U EJ2) Jl =2 J2 =1 k Ji-1 jl ~" j2;~" and N1 analogously corresponds to the 1-times s u m m a t i o n of m(Ejl U .

0 8 : t P(E2) = U2 = 0 . 3 0 ; P(E3) = La = 0 . 2 0 ; P(E4) = U4 = 0 . 5 2 ~ P(Ex) = - 0 . 0 2 : t P(E2) = L2 = 0 . 1 0 ; P(Ea) = Ua = 0 . 3 0 ; P(E4) = U4 = 0 . 5 2 ~ P(E1) = 0 . 0 8 : si2 T h e s t r u c t u r e S* of this 4 - P R I is therefore t h e set of all w e i g h t e d averages of t h e d i s t r i b u t i o n s sl to [] s12. No s i m p l e r description of S* is possible. 10): Let a 4 - P R I he: L1 =- 0 . 1 2 _< P(E1) _< 0 . 1 7 = Vl L2 = 0 . 1 5 _< P(E2) < 0 . 2 4 = u 2 L3 = 0 . 2 3 < P(E3) < 0 .

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A Methodology for Uncertainty in Knowledge-Based Systems by Kurt Weichselberger

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