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.

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.

Expert System Rough Sets Lower And Upper Approximations Uncertainity