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Using MCRDR based Agile approach for expert system development

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Abstract

Various expert system development approaches were proposed but most of them cannot deal with two problems: the difficulty of analysis and maintenance. Rather than to spend time waiting any longer, it is better to find an alternative solution from other research fields. In computer software development area, researchers have been suffering from the difficulty of maintenance and analysis, just as the researchers in the expert system development field. To solve this issue, researchers in the software used both agile software development and business rules approach: agile software development is for overcoming the the difficulty of analysis, and business rules approach is for reducing issues in the maintenance. There is a big opportunity that those two approaches can also be solve the two issues in the expert system development field. The paper describes requirements of the approach based on agile software development and the business rules approach. As a result, we consider and specify why the Multiple Classification Ripple Down Rules is the novel approach for the expert system development.

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References

  1. Abdelhamid Y, Hassan H, Rafea A (1997) A proposed methodology for expert system engineering. In: 5th International conference on artificial intelligence applications. Egyptians Computer Society, Cairo, Egypt

  2. Amine M, Ahmed-Nacer M (2011) An agile methodology for implementing knowledge management systems: A case study in component-based software engineering. Softw Eng Appl 5(4):159–170

    Google Scholar 

  3. Baumeister J, Puppe F, Seipel D (2004) An agile process model for developing diagnostic knowledge systems. Kunstliche Intelligenz 18(3):12–16

    Google Scholar 

  4. Bindoff I, Kang B, Ling T, Tenni P, Peterson G (2007) Applying mcrdr to a multidisciplinary domain. AI 2007: Advances in, Artificial Intelligence, pp 519–528

  5. Blythe J, Kim J, Ramachandran S, Gil Y (2001) An integrated environment for knowledge acquisition. In: Proceedings of the 6th international conference on Intelligent user interfaces, ACM, pp 13–20

  6. Burman S, Kim Y, Kang B, Park G (2006) Maintenance of game character’s ai by players. Int J Multimedia Ubiquitous Eng 1(1):39–46

    Google Scholar 

  7. Cao T, Martin E, Compton P (2004) On the convergence of incremental knowledge base construction. In: Discovery Science, Springer, pp 505–584

  8. Compton P, Jansen R (1990) A philosophical basis for knowledge acquisition. Knowledge acquisition 2(3):241–258

    Article  Google Scholar 

  9. Compton P, Peters L, Edwards G, Lavers T (2006) Experience with ripple-down rules. Knowl Based Syst 19(5):356–362

    Article  Google Scholar 

  10. Das P, Bhattacharyya D, Bandyopadhyay S, Kim T (2009) Analysis and diagnosis of breast cancer. u - and e - Service. Sci Technol 2(3):1–12

    Google Scholar 

  11. Grossner C, Preece A, Gokul Chander P, Radhakrishnan T, Suen C (1993) Exploring the structure of rule based systems. In: Proceedings of the national conference on artificial intelligence, Wiley, pp 704–704

  12. Han S, Chung H (2012) Social issue gives you an opportunity: Discovering the personalised relevance of social issues. Knowl Manage Acquisition Intell Syst 7457:272–284

    Article  Google Scholar 

  13. Hazzan O, Dubinsky Y (2007) Why software engineering programs should teach agile software development. ACM SIGSOFT Softw Eng Notes 32(2):1–3

    Article  Google Scholar 

  14. Jimènez Guarín C, Gòmez J (2010) Genesis: Agile generation of information management oriented software. Revista de Ingeniería 31:30–39

    Google Scholar 

  15. Kajko-Mattsson M (2008) Problems in agile trenches. In: Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement, ACM, pp 111–119

  16. Kang B (1996) Validating knowledge acquisition: multiple classification ripple-down rules. PhD thesis, University of New South Wales

  17. Kang B, Compton P, Preston P (1995) Multiple classification ripple down rules: evaluation and possibilities. In: The 9th knowledge acquisition for knowledge based systems workshop

  18. Kang B, Gambetta W, Compton P (1996) Verification and validation with ripple-down rules. Int J Human-Comput Stud 44(2):257–269

    Article  Google Scholar 

  19. Kim Y, Kang B (2008) Search query generation with mcrdr document classification knowledge. Practice and Patterns, Knowledge Engineering, pp 292–301

  20. Mateo R, Lee J, Gerardo B (2008) Healthcare expert system based on group cooperation model. Softw Eng Appl 2(1):105–116

    Google Scholar 

  21. Miranda-Mena T, Benítez US, Ochoa J, Martínez-Béjar R, Fernández-Breis J, Salinas J (2006) A knowledge-based approach to assign breast cancer treatments in oncology units. Expert systems with applications 31(3):451–457

    Article  Google Scholar 

  22. Mohammad A, Al Saiyd N (2010) A framework for expert knowledge acquisition. IJCSNS 10(11):145

    Google Scholar 

  23. Park S, Kim Y, Kang B (2004) Web document classification: Managing context change. In: IADIS International Conference WWW/Internet, pp 143–151

  24. Piaget J, Vonèche J (2007) The child’s conception of the world. Rowman & Littlefield Pub Incorporated, Lanham

    Google Scholar 

  25. Rao K, Kavita Naidu G (2011) A study of the agile software development methods, applicability and implications in industry. Softw Eng Appl 5(2):35–46

    Google Scholar 

  26. Schreiber G, Wielinga B, de Hoog R, Akkermans H, Van de Velde W (1994) Commonkads: a comprehensive methodology for kbs development. IEEE Expert 9(6):28–37

    Article  Google Scholar 

  27. Vazey M, Richards D (2005) Troubleshooting at the call centre: a knowledge-based approach. In: Proceedings of the Artificial Intelligence and Applications

  28. Wobcke W, Chan R, Limaru A, (2006) A call handling assistant for mobile devices. In: Intelligent Agent Technology, (2006) IAT’06. IEEE/WIC/ACM International Conference on IEEE, pp 717–722

  29. Zelkowitz M, Yeh R, Hamlet R, Gannon J, Basili V (1984) Software engineering practices in the USA and Japan. Computer 17(6):57–70

    Article  Google Scholar 

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Acknowledgments

This paper was supported by Australian Research Council (ARC) and Asian Office of Aerospace Research and Development (AOARD).

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Authors and Affiliations

  1. University of Tasmania, Hobart, Australia

    Soyeon Caren Han & Byeong Ho Kang

  2. Kyungpook National University, Daegu, Korea

    Hee-Geun Yoon & Seong-Bae Park

Authors
  1. Soyeon Caren Han
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  2. Hee-Geun Yoon
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  3. Byeong Ho Kang
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  4. Seong-Bae Park
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Correspondence to Soyeon Caren Han.

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Han, S.C., Yoon, HG., Kang, B.H. et al. Using MCRDR based Agile approach for expert system development. Computing 96, 897–908 (2014). https://doi.org/10.1007/s00607-013-0336-y

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