Advertisement

Visual Design of Drools Rule Bases Using the XTT2 Method

  • Krzysztof Kaczor
  • Grzegorz Jacek Nalepa
  • Łukasz Łysik
  • Krzysztof Kluza
Part of the Studies in Computational Intelligence book series (SCI, volume 381)

Abstract

Drools is one of the most popular expert system frameworks. It uses rules in several formats as knowledge representation. However, Drools has several limitations. It does not support visual modeling of rules. Moreover, it does not assure quality of the knowledge base. In this paper an alternative way of knowledge base designing for Drools is proposed. The method extends the Drools design process by using the XTT2 rule representation and the HQEd visual rule editor. To deal with the differences between the XTT2 and Drools representations, the Drools Export Plugin for HQEd has been implemented. The proposed approach overcomes limitations of the Drools design methodology. Furthermore, it provides a visual rule design editor for Drools and supports formal verification of the model using an optional module.

Keywords

Knowledge Base Rule Base Decision Table Visual Design Inference Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Giarratano, J.C., Riley, G.D.: Expert Systems. Thomson (2005)Google Scholar
  2. 2.
    Browne, P.: JBoss Drools Business Rules. Packt Publishing (2009)Google Scholar
  3. 3.
    Nalepa, G.J., Ligęza, A.: HeKatE methodology, hybrid engineering of intelligent systems. International Journal of Applied Mathematics and Computer Science 20(1), 35–53 (2010)CrossRefGoogle Scholar
  4. 4.
    Ligęza, A.: Logical Foundations for Rule-Based Systems. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  5. 5.
    Ligęza, A., Nalepa, G.J.: Proposal of a formal verification framework for the XTT2 rule bases. In: Tadeusiewicz, R., Ligęza, A., Mitkowski, W., Szymkat, M. (eds.) CMS 2009: Computer Methods and Systems: 7th Conference, Kraków, Poland, Kraków, November 26-27, pp. 105–110. AGH University of Science and Technology, Cracow, Oprogramowanie Naukowo-Techniczne (2009)Google Scholar
  6. 6.
    Kaczor, K., Nalepa, G.J.: HaDEs – presentation of the HeKatE design environment. In: Baumeister, J., Nalepa, G.J. (eds.) 5th Workshop on Knowledge Engineering and Software Engineering (KESE2009) at the 32nd German Conference on Artificial Intelligence, Paderborn, Germany, September 15, pp. 57–62 (2009)Google Scholar
  7. 7.
    Kaczor, K., Nalepa, G.J.: Design and implementation of HQEd, the visual editor for the XTT+ rule design method. Technical Report CSLTR 02/2008, AGH University of Science and Technology (2008)Google Scholar
  8. 8.
    Nalepa, G.J.: Architecture of the heaRT hybrid rule engine. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS, vol. 6114, pp. 598–605. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Gerrits, R., Spreeuwenberg, S.: Valens: A knowledge based tool to validate and verify an aion knowledge base. In: ECAI 2000, Proceedings of the 14th European Conference on Artificial Intelligence, Berlin, Germany, August 20-25, pp. 731–738 (2000)Google Scholar
  10. 10.
    Pascalau, E., Giurca, A.: Can URML model successfully drools rules? In: Giurca, A., Analyti, A., Wagner, G. (eds.) ECAI 2008: 18th European Conference on Artificial Intelligence: 2nd East European Workshop on Rule-based applicat ions, RuleApps2008, July 22, pp. 19–23. University of Patras, Patras (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Krzysztof Kaczor
    • 1
  • Grzegorz Jacek Nalepa
    • 1
  • Łukasz Łysik
    • 1
  • Krzysztof Kluza
    • 1
  1. 1.AGH University of Science and TechnologyKrakowPoland

Personalised recommendations