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Reseach Article

Uncertainty Classification of Expert Systems - A Rough Set Approach

Published on May 2012 by B. S. Panda, Rahuk Abhishek, S. S. Gantayat
National Conference on Advancement of Technologies – Information Systems and Computer Networks
Foundation of Computer Science USA
ISCON - Number 2
May 2012
Authors: B. S. Panda, Rahuk Abhishek, S. S. Gantayat
0dbd37d9-6c4c-4427-ae55-a14f6eed3755

B. S. Panda, Rahuk Abhishek, S. S. Gantayat . Uncertainty Classification of Expert Systems - A Rough Set Approach. National Conference on Advancement of Technologies – Information Systems and Computer Networks. ISCON, 2 (May 2012), 12-15.

@article{
author = { B. S. Panda, Rahuk Abhishek, S. S. Gantayat },
title = { Uncertainty Classification of Expert Systems - A Rough Set Approach },
journal = { National Conference on Advancement of Technologies – Information Systems and Computer Networks },
issue_date = { May 2012 },
volume = { ISCON },
number = { 2 },
month = { May },
year = { 2012 },
issn = 0975-8887,
pages = { 12-15 },
numpages = 4,
url = { /proceedings/iscon/number2/6464-1011/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advancement of Technologies – Information Systems and Computer Networks
%A B. S. Panda
%A Rahuk Abhishek
%A S. S. Gantayat
%T Uncertainty Classification of Expert Systems - A Rough Set Approach
%J National Conference on Advancement of Technologies – Information Systems and Computer Networks
%@ 0975-8887
%V ISCON
%N 2
%P 12-15
%D 2012
%I International Journal of Computer Applications
Abstract

In this paper, we discussed about the uncertainty classifications of the Expert Systems using a Rough Set Approach. It is a Softcomputing technique using this we classified the types of Expert Systems. An expert system has a unique structure, different from traditional programs. It is divided into two parts, one fixed, independent of the expert system: the inference engine, and one variable: the knowledge base. To run an expert system, the engine reasons about the knowledge base like a human. In the 80's a third part appeared: a dialog interface to communicate with users. This ability to conduct a conversation with users was later called "conversational". Rough set theory is a technique deals with uncertainty.

References
  1. Cheeseman, P. (1986) :"Probabilistic vs. Fuzzy Reasoning", in Uncertainity in AI, L. N. Kanal and J. F. Lemmer, eds. , Elsevier Scince Publichers, New York, N. Y. , pp. 85-102.
  2. Chelsea, MI. (1989), Dynamic Modelling and Expert Systems in Wastewater Engineering. Lewis Publishers, Inc. , pp. 167-192.
  3. Finn, G. A. (1989). Applications of Expert Systems in the Process Industry. In: G. G. Patry and D. Chapman (eds. ).
  4. Grzymala-Busse, J. (1988): LERS - A system for learning from examples based on rough sets, J. Intelligent and Robotics Systems, 1, pp. 3-16.
  5. Grzymala-Busse, J. (1988): Knowledge Acquisition Under Uncertainty – A Rough Set Approach, J. Intelligent and Robotics Systems, 1, pp. 3-16.
  6. Hayes-Roth, F. , D. A. Watchman, and D. B. Lenat, eds. (1983), Building Expert Systems, Addison-Wesley, Reading, Mass.
  7. Hyde, Andrew Dean (Sept 28, 2010), "The future of Artificial Intelligence".
  8. Ishizuka, M. , K. S. Fu, and J. T. P. Ya, (1982) "A Rule-Based Inference with Fuzzy Set for Structural Damage Assessment", in Approximate Reasonin in Decision Analysis, M. M. Gupta and E. Sanchez, eds. , Elsevier North-Holland, New York, N. Y, pp. 261-268.
  9. Jackson, Peter (1998), Introduction to Expert Systems (3rd Ed. ), Addison Wesley, New-Delhi.
  10. Nikolopoulos, Chris (1997), "Expert Systems: Introduction To First And Second Generation And Hybrid Knowledge Based Systems", Mercell Dekker INC.
  11. Panda, G. K. , Mitra, A. (2008): Rough Set Application in Social Network using Rule Induction. In: Proceedings of NCETIT, India, pp. 59-64.
  12. Panda, G. K. , Panda, B. S. (2009), "Preserving privacy in social networks – A Rough Set Approach", In: Proceedings of ICONCT-09, MEPCO Shlenck Eng. College, Sivakasi, India, pp. 315-320,
  13. Patterson, Dan W, (2007), "Introduction to Artificial Intelligence & Expert Systems", Prentice-Hall India, New Delhi.
  14. Pawlak, Z. (1982): Rough Sets. J. Inf. & Comp. Sc. II, 341- 356.
  15. Pawlak, Z. (1991): Rough Sets-Theoretical Aspects of Reasoning about Data. Kluwer Acad Publ.
  16. Pawlak, Z. , Skowron, A. : Rough sets- Some Extensions. J. Information Sciences. 177(1), 28-40, 2007.
  17. Rich, Elaine, and Kevin Knight, (2006), "Artificial Intelligence", McGraw Hills Inc.
  18. Sweeney, L. (2002): K-anonymity: A model for protecting privacy. Int. J. Uncertainty, Fuzziness and Knowledge-based System, vol. 10, no. 5, pp. 557-570.
  19. Wise, B. P. and M. Henrion, (1986), "A Framework for Comparing Uncertain Inference Systems to Probability", in Uncertainty in AI, L. N. Kanal and J. F. Lemmer, eds. , Elsevier Science Publishers, New York, N. Y. , pp. 69-83.
  20. Zadeh, L. A, , (1986) "Is probability Theory Sufficient for Dealing with Uncertainty in AI: A Negative View," in Uncertainty in AI, Elsevier Science Publishers, New York, NY, pp. 103-116.
  21. Zadeh, L. A. , "Is Probability Theory Sufficient for Dealing with Uncertainty in AI: A Negative View" in Uncertainty in AI, L. N. Kanal and J. F. Lemmer, eds. , Elsevier Science Publishers, New York, N. Y. , 1986, pp. 103-116.
  22. Zhu, W (2007): Topological Approaches to Covering Rough Sets. , J. Information Sciences. (USA), 177, 1499-1508.
Index Terms

Computer Science
Information Sciences

Keywords

Expert System Rough Sets Lower And Upper Approximations Uncertainity