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2010 | 20 | 1 | 35-53
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The HeKatE methodology. Hybrid engineering of intelligent systems

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This paper describes a new approach, the HeKatE methodology, to the design and development of complex rule-based systems for control and decision support. The main paradigm for rule representation, namely, eXtended Tabular Trees (XTT), ensures high density and transparency of visual knowledge representation. Contrary to traditional, flat rule-based systems, the XTT approach is focused on groups of similar rules rather than on single rules. Such groups form decision tables which are connected into a network for inference. Efficient inference is assured as only the rules necessary for achieving the goal, identified by the context of inference and partial order among tables, are fired. In the paper a new version of the language-XTT² - is presented. It is based on ALSV(FD) logic, also described in the paper. Another distinctive feature of the presented approach is a top-down design methodology based on successive refinement of the project. It starts with Attribute Relationship Diagram (ARD) development. Such a diagram represents relationships between system variables. Based on the ARD scheme, XTT tables and links between them are generated. The tables are filled with expert-provided constraints on values of the attributes. The code for rule representation is generated in a humanreadable representation called HMR and interpreted with a provided inference engine called HeaRT. A set of software tools supporting the visual design and development stages is described in brief.
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  • Department of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland
  • Department of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland
  • Ben-Ari, M. (2001). Mathematical Logic for Computer Science, Springer-Verlag, London.
  • Browne, P. (2009). JBoss Drools Business Rules, Packt Publishing, Birmingham.
  • Bratko, I. (2000). Prolog Programming for Artificial Intelligence, 3rd Edn, Addison Wesley, Harlow.
  • Brownston, L., Farrell, R., Kant, E. and Martin, N. (1985). Programming Expert Systems in OPS5, Addison-Wesley, Reading, MA/Menlo Park, CA.
  • Burbeck, S. (1992). Applications programming in smalltalk80(tm): How to use model-view-controller (MVC), Technical report, Department of Computer Science, University of Illinois, Urbana-Champaign, IL.
  • Cheng, A. M. K. (2002). Real-Time Systems. Scheduling, Analysis and Verification, John Wiley & Sons, Inc., Hoboken, NJ.
  • Clark, J. (1999). Xsl transformations (xslt) version 1.0 w3c recommendation 16 November 1999, Technical report, World Wide Web Consortium (W3C).
  • Connolly, T., Begg, C. and Strechan, A. (1999). Database Systems. A Practical Approach to Design, Implementation, and Management, 2nd Edn, Addison-Wesley, Harlow/Reading, MA.
  • Coenen, F., et al. (2000). Validation and verification of knowledge-based systems: Report on eurovav99, The Knowledge Engineering Review 15(2): 187-196.
  • Forgy, C. (1982). Rete: A fast algorithm for the many patterns/many objects match problem, Artificial Intelligence 19(1): 17-37.
  • Friedman-Hill, E. (2003). Jess in Action, Rule Based Systems in Java, Manning, Greenwich, CT.
  • Gamma, E., Helm, R., Johnson, R. and Vlissides, J. (1995). Design Patterns, 1st Edn, Addison-Wesley Pub Co, Reading, MA.
  • Genesereth, M. R. and Nilsson, N. J. (1987). Logical Foundations for Artificial Intelligence, Morgan Kaufmann Publishers, Inc., Los Altos, CA.
  • Giarratano, J. C. and Riley, G. D. (2005). Expert Systems, Thomson, Boston, MA.
  • Giurca, A., Gasevic, D. and Taveter, K. (Eds) (2009). Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches, Information Science Reference, Hershey, New York, NY.
  • Horrocks, I., Patel-Schneider, P. F., Boley, H., Tabet, S., Grosof, B. and Dean, M. (2004). SWRL: A semantic web rule language combining OWL and RuleML: W3C member submission 21 may 2004, Technical report, W3C.
  • Jackson, P. (1999). Introduction to Expert Systems, 3rd Edn, Addison-Wesley, Harlow.
  • Kaczor, K. (2008). Design and implementation of a unified rule base editor, M.Sc. thesis, AGH University of Science and Technology, Cracow.
  • Kaczor, K. and Nalepa, G. J. (2008). 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, Cracow.
  • Klösgen, W. and Żytkow, J. M. (Eds) (2002). Handbook of Data Mining and Knowledge Discovery, Oxford University Press, New York, NY.
  • Laffey etal, T. (1988). Real-time knowledge-based systems, AI Magazine Spring: 27-45.
  • Liebowitz, J. (Ed.) (1998). The Handbook of Applied Expert Systems, CRC Press, Boca Raton, FL.
  • Ligęza, A. (1986). An expert systems approach to analysis and control in certain complex systems, Preprints of the 4-th IFAC/IFIP Symposium on Software for Computer Control SOCOCO'86, Graz, Austria, pp. 147-152.
  • Ligęza, A. (1988). Expert systems approach to decision support, European Journal of Operational Research 37(1): 100-110.
  • Ligęza, A. (1993). Logical foundations for knowledge-based control systems-Knowledge representation, reasoning and theoretical properties, Scientific Bulletins of AGH: Automatics 63(1529): 144.
  • Ligęza, A. (1996). Logical support for design of rule-based systems. Reliability and quality issues, in M. Rousset (Ed.), ECAI-96 Workshop on Validation, Verification and Refinment of Knowledge-based Systems, Vol. W2, ECCAI (European Coordination Committee for Artificial Intelligence), Budapest, pp. 28-34.
  • Ligęza, A. (1998). Towards logical analysis of tabular rule-based systems, Proceedings of the Ninth European International Workshop on Database and Expert Systems Applications, Vienna, Austria, pp. 30-35.
  • Ligęza, A. (1999). Validation and Verification of Knowledge Based Systems: Theory, Tools and Practice, Kluwer Academic Publishers, Boston, MA/Dordrecht/London, pp. 313-325.
  • Ligęza, A. (2001). Toward logical analysis of tabular rulebased systems, International Journal of Intelligent Systems 16(3): 333-360.
  • Ligęza, A. (2005). Logical Foundations for Rule-Based Systems, AGH University of Science and Technology Press, Cracow.
  • Ligęza, A. (2006). Logical Foundations for Rule-Based Systems, Springer-Verlag, Berlin/Heidelberg.
  • Ligęza, A. and Nalepa, G. J. (2005). Visual design and on-line verification of tabular rule-based systems with XTT, in K. P. Jantke, K.-P. Fähnrich and W. S. Wittig (Eds), Marktplatz Internet: Von e-Learning bis e-Payment: 13. Leipziger Informatik-Tage, LIT 2005, Lecture Notes in Informatics (LNI), Gesellschaft fur Informatik, Bonn, pp. 303-312.
  • Ligęza, A. and Nalepa, G. J. (2007). Knowledge representation with granular attributive logic for XTT-based expert systems, in D. C. Wilson, G. C. J. Sutcliffe and FLAIRS (Eds), FLAIRS-20: Proceedings of the 20th International Florida Artificial Intelligence Research Society Conference: Key West, Florida, May 7-9, 2007, Florida Artificial Intelligence Research Society, AAAI Press, Menlo Park, CA, pp. 530-535.
  • Ligęza, A. and Nalepa, G. J. (2008). Granular logic with variables for implementation of extended tabular trees, in D. C. Wilson and H. C. Lane (Eds), FLAIRS-21: Proceedings of the Twenty-First International Florida Artificial Intelligence Research Society conference: 15-17 May 2008, Coconut Grove, Florida, USA, AAAI Press, Menlo Park, CA, pp. 341-346.
  • Ligęza, A., Wojnicki, I. and Nalepa, G. J. (2001). Tab-trees: A case tool for design of extended tabular systems, in H.C. Mayr, J. Lazansky, G. Quirchmayr and P. Vogel (Eds), Database and Expert Systems Applications, Lecture Notes in Computer Sciences, Vol. 2113, Springer-Verlag, Berlin, pp. 422-431.
  • Ligęza, A. (1996 ). Logical Support for Design of Rule-Based Systems. Reliability and Quality Issues LAAS, Report No. 96170, Toulouse.
  • Morgan, T. (2002). Business Rules and Information Systems. Aligning IT with Business Goals, Addison Wesley, Boston, MA.
  • Nalepa, G. J. (2004). Meta-Level Approach to Integrated Process of Design and Implementation of Rule-Based Systems, PhD thesis, AGH University of Science and Technology, Institute of Automatics, Cracow.
  • Nalepa, G. J. and Ligęza, A. (2005a). A graphical tabular model for rule-based logic programming and verification, Systems Science 31(2): 89-95.
  • Nalepa, G. J. and Ligęza, A. (2005b). Software Engineering: Evolution and Emerging Technologies, Frontiers in Artificial Intelligence and Applications, Vol. 130, IOS Press, Amsterdam, pp. 330-340.
  • Nalepa, G. J. and Ligęza, A. (2005c). A visual edition tool for design and verification of knowledge in rule-based systems, Systems Science 31(3): 103-109.
  • Nalepa, G. J. and Ligęza, A. (2006). Prolog-based analysis of tabular rule-based systems with the “xtt” approach, in G. C. J. Sutcliffe and R. G. Goebel (Eds), FLAIRS 2006: Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference: Melbourne Beach, Florida, May 11-13, 2006, Florida Artificial Intelligence Research Society, AAAI Press, Menlo Park, CA, pp. 426-431.
  • Nalepa, G. J. and Ligęza, A. (2008). Xtt+ rule design using the alsv(fd), in A. Giurca, A. Analyti and G. Wagner (Eds), ECAI 2008: 18th European Conference on Artificial Intelligence: 2nd East European Workshop on Rule-based Applications, RuleApps2008: Patras, 22 July 2008, University of Patras, Patras, pp. 11-15.
  • Nalepa, G. J., Ligęza, A., Kaczor, K. and Furmańska, W. T. (2009). Hekate rule runtime and design framework, in G. W. Adrian Giurca and G.J. Nalepa (Eds), Proceedings of the 3rd East European Workshop on Rule-Based Applications (RuleApps 2009), Cottbus, Germany, September 21, 2009, BTU Cottbus, Cottbus, pp. 21-30.
  • Pawlak, Z. (1991). Rough Sets. Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht/Boston, MA/London.
  • Quinlan, J. R. (1987). Simplifying decision trees, International Journal of Man-Machine Studies 27(3): 221-234.
  • Ross, R. G. (2003). Principles of the Business Rule Approach, 1st Edn, Addison-Wesley Professional, Reading, MA.
  • Torsun, I. S. (1995). Foundations of Intelligent KnowledgeBased Systems, Academic Press, London/San Diego, CA/New York, NY/Boston, MA/Sydney/Tokyo/Toronto.
  • Tzafestas, S. and Ligęza, A. (1988). Expert control through decision making, Foundations of Control Engineering 13(1): 43-51.
  • Tzafestas, S. and Ligęza, A. (1989). Expert control through decision making, Journal of Intelligent and Robotic Systems 1(4): 407-425.
  • van Harmelen, F., Lifschitz, V. andPorter, B. (Eds) (2007). Handbook of Knowledge Representation, Elsevier Science, Amsterdam.
  • van Harmelen, F. (1996). Applying rule-based anomalies to kads inference structures, ECAI'96 Workshop on Validation, Verification and Refinement of Knowledge-Based Systems, Budapest, Hungary, pp. 41-46.
  • von Halle, B. (2001). Business Rules Applied: Building Better Systems Using the Business Rules Approach, Wiley, New York, NY.
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