Skip to main content
SpringerLink
Log in

An Interval-Valued Confidence for Inference in Hybrid Knowledge-Based Systems

  • Chapter
Knowledge-Based Information Systems in Practice

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 30))

Abstract

Knowledge Based System (KBS) is a problem solving approach that makes use of human knowledge in decision strategies. Modeling and representing imperfect human knowledge associated with uncertainty is an important task in KBS development. There are various types of uncertainty, and randomness and fuzziness are among the most important. Handling hybrid uncertainty in one KBS is critical to support real world applications. Knowware System (KWS) is an intelligent tool designed to support application developers in constructing customized hybrid KBS without requiring developers being familiar with relevant intelligent techniques. It is essential for KWS to construct corresponding inference structure in resulting KBS and process the inference with hybrid uncertainty. To fulfill this requirement the extended Truth Value Flow Inference (TVFI) and Interval-Valued Confidence (IVC) have been defined and developed as ambedded mechanisms of KWS, and the hybrid logic has been adopted for the framework of handling hybrid uncertainty.

This chapter discusses the IVC for the inference in hybrid KBS constructed by KWS with hierarchical knowledge representation. The knowledge content (precise or imprecise) represented in multiple units of knowledge hierarchy and the confidence obtained during inference process are treated as at two levels separately but simultaneously based on the extended TVFI. With the basic form of IVC, a fuzzy truth value is represented as a fuzzy number defined as a three-parametric triangular type-1 fuzzy set on the unit interval [0, 1]. The inference with both fuzzy truth and probability results an Extended Interval-Valued Confidence (EIVC) which is an interval type-2 fuzzy set on [0, 1] having the probability as an uncertainty measure on the fuzzy truth. The main focus of discussion will be put on interval-valued confidence. To provide the background of discussion, the KWS scheme, the extended TVFI and the concepts of hybrid logic will be briefly introduced.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Lu, R.: From hardware to software to knowware: It’s third liberation? IEEE Intelligent Systems 20(2), 82 – 85 (2005)

    Google Scholar 

  2. Ding, L.: A model of hierarchical knowledge representation – toward knowware for intelligent systems. J. Advanced Computational Intelligence and Intelligent Informatics 11(10), 1232–1240 (2007)

    Google Scholar 

  3. Kendal, S., Creen, M.: An Introduction to Knowledge Engineering. Springer (2007)

    Google Scholar 

  4. Ding, L.: Design and development of knowware system. In: 2nd Int. Conf. on Innovative Computing, Information and Control, pp. 17–21 (2007)

    Google Scholar 

  5. Ding, L., Lo, S.L.: Truth value flow inference in hybrid KBS constructed by KWS. In: 3rd Int. Conf. Innovative Computing, Information and Control, pp. 311–315 (2008)

    Google Scholar 

  6. Kasabov, N.K.: Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, 1st edn. MIT Press, Cambridge (1996)

    Google Scholar 

  7. Roventa, E., Spircu, T.: Management of Knowledge Imperfection in Building Intelligent System. STUDFUZZ, vol. 227. Springer, Berlin (2009)

    Google Scholar 

  8. Durrett, R.: Probability: Theory and Examples (Probability: Theory & Examples), 3rd edn. Duxbury Press (2004)

    Google Scholar 

  9. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  10. Casella, G., Berger, R.: Statistical inference. Duxbury Press Belmont, Calif (1990)

    MATH  Google Scholar 

  11. Hailperin, T.: Probability logic. Notre Dame Journal of Formal Logic 25(3), 198–212 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  12. Nilsson, N.: Probabilistic logic. Artificial Intelligence 28, 71–87 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  13. Jordan, M., Weiss, Y.: Probabilistic inference in graphical models. In: Handbook of Neural Networks and Brain Theory (2002)

    Google Scholar 

  14. Buchanan, B.G., Shortliffe, E.H.: Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Buchann and Shortliffe (1984)

    Google Scholar 

  15. Hanss, M.: Applied Fuzzy Arithmetic: An Introduction with Engineering Applications. Springer (2010)

    Google Scholar 

  16. Takagi, T., Kawase, K.: A trial for data retrieval using conceptual fuzzy sets. IEEE Trans. Fuzzy Systems 9(4), 497–505 (2001)

    Article  Google Scholar 

  17. Wang, Y.: On concept algebra for computing with words (cww). Int. J. Semantic Computing 4(3), 331–356 (2010)

    Article  Google Scholar 

  18. Li, X., Liu, B.: Hybrid logic and uncertain logic. J. of Uncertain Systems 3(2), 83–94 (2009)

    Google Scholar 

  19. Ding, L.: Inference in hybrid KBS with interval-valued confidence. In: 2008 IEEE World Congress on Computational Intelligence 2008 IEEE Int. Conf. Fuzzy Systems, pp. 1350–1357 (2008)

    Google Scholar 

  20. Mendel, J.M.: Type-2 fuzzy sets and systems: An overview. IEEE Computational Intelligence 2(1), 22–29 (2007)

    MathSciNet  Google Scholar 

  21. Mendel, J.M.: On the importance of interval sets in type-2 fuzzy logic systems. In: Proc. Joint 9th IFSA World Congress 20th NAFIPS Int. Conf, pp. 1647–1652 (2001)

    Google Scholar 

  22. Mendel, J.M., John, R., Liu, F.: Interval type-2 fuzzy logic systems made simple. IEEE Trans. Fuzzy Systems 14(6), 808–821 (2006)

    Article  Google Scholar 

  23. Nguyen, H.T., Walker, E.A.: First Course in Fuzzy Logic. CRC Press, Boca Raton (1999)

    Google Scholar 

  24. Ding, L., Lo, S.L.: Inference in knowware system. In: 8th Int. Conf. Machine Learning and Cybernetics, pp. 215–220 (2009)

    Google Scholar 

  25. Meystel, A.M.: Principles of word-based knowledge representation and knowledge processing of CW. In: Wang, P.P. (ed.) Computing with Words, pp. 329–346. John Wiley and Sons, NJ (2001)

    Google Scholar 

  26. Meystel, A.M., Wang, P.P.: Computing with words: the problems and solutions. In: Wang, P.P. (ed.) Computing with Words, pp. 69–87. John Wiley and Sons, NJ (2001)

    Google Scholar 

  27. Zadeh, L.A.: From computing with numbers to computing with words – from manipulation of measurements to manipulation of perceptions. In: Wang, P.P. (ed.) Computing with Words, pp. 35–68. John Wiley and Sons, NJ (2001)

    Google Scholar 

  28. Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 90, 111–127 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  29. Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Systems 4, 103–111 (1996)

    Article  Google Scholar 

  30. Jang, J.R., Sun, C., Mizutani, E.: Neural-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, NJ (1997)

    Google Scholar 

  31. Yager, R.R., Filev, D.P.: Essentials of Fuzzy Modeling and Control. John and Wiley and Sons, NJ (1994)

    Google Scholar 

  32. Wang, P.Z., Zhang, H.M.: Truth value flow inference and its mathematical theory. In: Wang, P.Z., Loe, K.F. (eds.) Between Mind and Computer, pp. 325–358. World Scientific, Singapore (1993)

    Google Scholar 

  33. Ding, L., Shen, Z.: Neural network implementation of fuzzy inference for approximate case-based reasoning. In: Mitra, S., Gupta, M.M., Kraske, W. (eds.) Neural and Fuzzy Systems: The Emerging Science of Intelligence and Computing, pp. 28–56. SPIE Press (1994)

    Google Scholar 

  34. Liu, B.: Uncertainty Theory. Springer, Berlin (2007)

    MATH  Google Scholar 

  35. Watada, J., Wang, S., Pedrycz, W.: Building confidence-interval-based fuzzy random regression models. IEEE Trans. Fuzzy System 17(6), 1273–1283 (2009)

    Article  Google Scholar 

  36. Bergmann, M.: An Introduction to Many-Valued and Fuzzy Logic: Semantics, Algebras, and Derivation Systems. Cambridge University Press (2008)

    Google Scholar 

  37. Lo, S.L., Ding, L., Chen, Y.: Application of hybrid logic in inference of knowware system. In: 9th Int. Conf. on Machine Learning and Cybernetics, pp. 1078–1083 (2010)

    Google Scholar 

  38. Liu, B.: Uncertain entailment and modus ponens in the framework of uncertain logic. J. of Uncertain Systems 3(4), 243–251 (2009)

    Google Scholar 

  39. Zadeh, L.A.: Toward human level machine intelligence – is it achievable? IEEE Computational Intelligence 3(3), 11 – 22 (2008)

    Google Scholar 

  40. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning: Part 1. Information Sciences (8), 199–249 (1975)

    Google Scholar 

  41. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning: Part 2. Information Sciences (8), 301 – 357 (1975)

    Google Scholar 

  42. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning: Part 3. Information Sciences (9), 43 – 80 (1975)

    Google Scholar 

  43. Ding, L., Nadkarni, S.: Automatic construction of knowledge-based system using knowware system. In: 6th Int. Conf. on Machine Learning and Cybernetics, pp. 789–794 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore, Singapore, 119615

    Liya Ding

  2. Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau SAR, China

    Sio-Long Lo

Authors
  1. Liya Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sio-Long Lo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liya Ding .

Editor information

Editors and Affiliations

  1. Air Operations Division Defence Science and Technology Organisation, Edinburgh, South Australia, Australia

    Jeffrey W. Tweedale

  2. University of Canberra, Australia and University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  3. Waseda University, Fukuoka, Japan

    Junzo Watada

  4. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Ding, L., Lo, SL. (2015). An Interval-Valued Confidence for Inference in Hybrid Knowledge-Based Systems. In: Tweedale, J., Jain, L., Watada, J., Howlett, R. (eds) Knowledge-Based Information Systems in Practice. Smart Innovation, Systems and Technologies, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-319-13545-8_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13545-8_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13544-1

  • Online ISBN: 978-3-319-13545-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation