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A new evidence updating rule based on conditional event

  • Published:
Journal of Electronics (China)

Abstract

Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledge in AI. The appearance of uncertain reasoning urges us to measure the belief of rule. Now, most of uncertain reasoning models represent the belief of rule by conditional probability. However, it has many limitations when standard conditional probability is used to measure the belief of expert system rule. In this paper, AI rule is modelled by conditional event and the belief of rule is measured by conditional event probability, then we use random conditional event to construct a new evidence updating method. It can overcome the drawback of the existed methods that the forms of focal sets influence updating result. Some examples are given to illustrate the effectiveness of the proposed method.

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References

  1. E. C. Kulasekere, K. Premaratne, and D. A. Dewasurendra. Conditioning and updating evidence. Approximate Reasoning, 36(2004)1, 75–108.

    Article  MATH  MathSciNet  Google Scholar 

  2. G. Shafer and R. Logan. Implementing Dempster’s rule for hierarchical evidence. Artificial Intelligence, 33(1987)3, 271–298.

    Article  MATH  MathSciNet  Google Scholar 

  3. G. Shafer. Jeffrey’s rule of conditioning. Philosophy Science, 48(1981)9, 337–362.

    Article  MathSciNet  Google Scholar 

  4. P. Smets. The transferable belief model and random sets. IPMU: TBM-RS, 1992, 37–46.

  5. D. Dubios and H. Prade. Upadting with belief function, ordinal conditional functions and possibility measures. Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence, New York, July 27–29, 1990, 311–329.

  6. H. Ichihashi and H. Tanaka. Jeffrey-like rules of conditioning for the Dempster-Shafer theory of evidence. Approximate Reasoning, 3(1989)2, 143–156.

    Article  MATH  MathSciNet  Google Scholar 

  7. Yongchuan Tang and Jiacheng Zhang. Genneralized Jeffrey’s rule of conditioning and evidence combining rule for a priori probabilistic knowledge in conditional evidence theory. Information Sciences, 176 (2006)11, 1590–1606.

    Article  MATH  MathSciNet  Google Scholar 

  8. K. Premaratne and D. A. Dewasurendra. Evidence combination in an environment with heterogeneous sources. IEEE Transactions on Systems, Man, and Cybernetics, 37(2007)3, 298–309.

    Article  Google Scholar 

  9. K. Premaratne and D. A. Dewasurendra. Evidence updating in a heterogeneous sensor environment. Circuits and Systems, 4(2003)3, 824–827.

    Google Scholar 

  10. D. Lewis. Probabilities of conditionals andconditional probabilities. Philosophy Reviews, 85(1976)7, 297–315.

    Article  Google Scholar 

  11. I. R. Goodman, R. Mahler and H. T. Nguyen. Mathematics of Data Fusion. Kluwer Academic Publishers, 1997, 345–379.

  12. M. Spies. Conditional events, conditioning, and random sets. IEEE Transactions on Systems, Man, and Cybernetics, 24(1994)12, 1755–1763.

    Article  MathSciNet  Google Scholar 

  13. R. Mahler. Combining ambiguous evidence with respect to ambiguous a priori knowledge, Part I: Boolean Logic. IEEE Transactions on Systems, Man, and Cybernetics, 26(1996)1, 27–41.

    Article  Google Scholar 

  14. Deng Yong, Liu Qi, and Shi Wenkang. A review on theory of conditional event algebra. Chinese Journal of Computers, 26(2003)6, 650–661 (in Chinese). 邓勇, 刘琪, 施文康. 条件事件代数研究综述. 计算机学报, 26(2003)6, 650–661.

    MathSciNet  Google Scholar 

  15. Deng Yong and Shi Wenkang. GNW conditional event algebra and its application. Computer Engineering, 28(2002)1, 23–25 (in Chinese).. 邓勇, 施文康. GNW 条件事件代数的原理和应用. 计算机工程, 28(2002)1, 23–25.

    MathSciNet  Google Scholar 

  16. I. R. Goodman, H. T. Nguyen, and E. A. Walker. Conditional inference and logic for intelligence systems. A Theory of Measure-Free Conditioning. 1991, 137–174.

  17. R. Mahler. Combining ambiguous evidence with respect to ambiguous a priori knowledge, Part II: Fuzzy logic. Fuzzy Sets and Systems, 75(1995)1, 319–354.

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China

    Chenglin Wen, Yingchang Wang & Xiaobin Xu

  2. Institute of Information and Control, Hangzhou Dianzi University, HangZhou, 310018, China

    Yingchang Wang

Authors
  1. Chenglin Wen
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  2. Yingchang Wang
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  3. Xiaobin Xu
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Corresponding author

Correspondence to Yingchang Wang.

Additional information

Supported by the NSFC (No. 60772006, 60874105), the ZJNSF (Y1080422, R106745), and Aviation Science Foundation (20070511001).

Communication author: Wang Yingchang, born in 1981, male, postgraduate.

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Wen, C., Wang, Y. & Xu, X. A new evidence updating rule based on conditional event. J. Electron.(China) 26, 731–737 (2009). https://doi.org/10.1007/s11767-009-0091-6

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  • DOI: https://doi.org/10.1007/s11767-009-0091-6

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