Article

Article title EFFECTIVE CONROL-BASED EXPERT SYSTEM USING COGNITIVE MODELS
Authors A.N. Tselykh, V.S. Vasilev, L.A. Tselykh, S.A. Barkovskii
Section SECTION II. INTELLIGENT DECISION SUPPORT AND CONTROL
Month, Year 03, 2017 @en
Index UDC 004.891.2
DOI
Abstract In this paper we present a composite module of knowledge acquisition for a productive expert system based on effective controls (ECES). The operation of the module is aimed at discovering new knowledge from the conceptual domain model, i.e., from transformed expert knowledge. We propose to integrate the methodology of cognitive simulation and processing algorithms into a single mechanism of effective controls, which allows revealing the hidden knowledge of the system under investigation. This mechanism combines three aspects: the system representation of the domain, the mathematical support and the knowledge retrieval automation technique. Fuzzy cog-nitive maps can fully lay out the problem in hand in the aspect of understanding of its structure and relationships. The algorithmic processing unit is designed to handle fuzzy directed weighted signed graphs with feedback loops: selection of clusters, identifying the most important factors of influence and impact, formation of a set of relevant factors. This set is the basis for the development of relevant antecedents and consequents for ES fuzzy rules based on the mechanism of fuzzy inference. This paper presents the ECES implementation in a numerical experiment using an algorithm of effective control based on systems theory and systems analysis, and adapted to address the control problems in the socio-economic systems. It is shown that the algorithm is computationally efficient and applicable for the given domain. Reducing user subjectivity was a priority in the design of the ECES. The obtained results increase the intelligence of the logical inference. The ECES prototype was modeled with a focus on user requirements and the maximum response of the information system based on simplified hybrid flexible iterative approach. The technology of realization of the information system includes the use of MySQL relational database management system, a modified error reporting system taking into account the application type, standardization of the UML 2.4.1 language and the UML-diagram.

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Keywords Expert systems; effect control; knowledge acquisition; knowledge discovery.
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14. Guillaume S. and Charnomordic B. Fuzzy inference systems: An integrated modeling environment for collaboration between expert knowledge and data using FisPro // Expert Systems with Applications. – 2012. – Vol. 39. – P. 8744-8755.
15. Kim J.S. Development of a Composite Knowledge Manipulation Tool: K-Expert // Expert Systems with Applications. – 2014. – Vol. 41. – P. 4337-4348.
16. Casillas J., Cordón O., Herrera F. and Magdalena L. Interpretability Improvements to Find the Balance Interpretability-Accuracy in Fuzzy Modeling: An Overview // in Accuracy Improvements in Linguistic Fuzzy Modeling. Series Studies in Fuzziness and Soft Computing. – Berlin: Springer, 2003. – Vol. 129. – P. 3-24.
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REFERENCES
1. Hayes-Roth F., Waterman D.A. a Lenat D.B. Building Expert Systems. New York: Addison-Wesley, 1983, 443 p.
2. Feigenbaum E. The art of artificial intelligence: themes and case studies of knowledge engineering, in Proceedings of the Fifth International Joint Conference on Artificial Intelligence, Cambridge, MA, 1977.
3. Leu G. and Abbass H. A multi-disciplinary review of knowledge acquisition methods: From human to autonomous eliciting agents, Knowledge-Based Systems, 2016, Vol. 105, pp. 1-22.
4. Altay A. and Kayakutlu G. Fuzzy cognitive mapping in factor elimination: A case study for innovative power and risks, in WCIT-2010. Procedia Computer Science, Istanbul, Turkey, 2011.
5. Tselykh A.N., Vasil'ev V.S., Tselykh L.A. i Barkovskiy S.A. Metod vydeleniya effektivnykh upravleniy v nechetkikh kognitivnykh modelyakh, predstavlenykh orientirovannymi vzveshennymi grafami [Method of the selection of effective controls in fuzzy cognitive models represented as directed weighted graphs], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2016, No. 5 (178), pp. 5-19.
6. Lee K.C. and Lee S. A causal knowledge-based expert system for planning an Internet-based stock trading system, Expert Systems with Applications, 2012, Vol. 39, pp. 8526-8635.
7. Gomez A., A. Morenoa, J. Parosa and Sierra-Alonso A. Knowledge maps: An essential technique for conceptualization, Data & Knowledge Engineering, 2000, Vol. 33, pp 169-190.
8. Rafea A., Hassen H. and Hazman M. Automatic knowledge acquisition tool for irrigation and fertilization expert systems, Expert Systems with Applications, 2003, Vol. 24, pp. 49-57.
9. Ozdena A., Faghria A. and Li M. Using Knowledge-Automation Expert Systems to Enhance the Use and Understanding of Traffic Monitoring Data in State DOTs, in International Conference on Sustainable Design, Engineering and Construction. Procedia Engineering, 2016.
10. Ruiz-Mezcua B., Garsia-Crespo A., Lopez-Cuadrado J.L. and Gonzalez-Carrasco I. An expert system development tool for non AI experts, Expert Systems with Applications, 2011, Vol. 38, P. 597-609.
11. Do Rosário C.R., Kipper L.M., Frozza R. and Mariani B.B. Modeling of tacit knowledge in industry: Simulations on the variables of industrial processes, Expert Systems with Applications, 2015, Vol. 145, Issue 3, pp. 1613-1625.
12. Akinnuwesi B.A., Uzoka F.-M.E., Olabiyisi S.O. and Omidora E.O. A framework for user-centric model for evaluating the performance of distributed software system architecture, Expert Systems with Applications, 2012, Vol. 39, pp. 9323-9339.
13. Akinnuwesi B.A., Uzoka F.-M.E. and Osamiluyi A.O. Neuro-Fuzzy Expert System for evaluating the performance of Distributed Software System Architecture, Expert Systems with Applications, 2013, Vol. 40, Issue 9, pp. 3313-3327.
14. Guillaume S. and Charnomordic B. Fuzzy inference systems: An integrated modeling environment for collaboration between expert knowledge and data using FisPro, Expert Systems with Applications, 2012, Vol. 39, pp. 8744-8755.
15. Kim J.S. Development of a Composite Knowledge Manipulation Tool: K-Expert, Expert Systems with Applications, 2014, Vol. 41, pp. 4337-4348.
16. Casillas J., Cordón O., Herrera F. and Magdalena L. Interpretability Improvements to Find the Balance Interpretability-Accuracy in Fuzzy Modeling: An Overview, in Accuracy Improvements in Linguistic Fuzzy Modeling. Series Studies in Fuzziness and Soft Computing. Berlin: Springer, 2003, Vol. 129, pp. 3-24.
17. Breuker J. A cognitive science perspective on knowledge acquisition, International Journal of Human-Computer Studies, 2013, Vol. 71, pp. 177-183.
18. Kroenke M.D. Database processing – Fundamentals, design and implementation. Addison Wesley, 2014, 640 p.
19. Muthitacharoen А. and Saeed A.K. Examining user involvement in continuous software development (a case of error reporting system), Communications of the ACM, 2009, Vol. 52 (9), pp. 113-117.
20. Larman, Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development. Lebanon, Indiana: Prentice Hall, 2004, 616 p.
21. Booch G., Rumbaugh J. and Jacobson I. Unified Modeling Language User Guide. Addison Wesley Professional, 2005, 496 p.
22. Ambler S.W. The Object Primer 3rd Edition: Agile Model Driven Development with UML 2. New York: Cambridge University Press, 2004, 572 p.
23. Tselykh A.N. i Tselykh L.A. Metodologiya sravnitel'nogo kognitivnogo modelirovaniya na osnove analiza nechetkikh tselevykh i upravlyayushchikh faktorov [Methodology for compara-tive cognitive modeling based on the analysis of fuzzy target and control factors], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2015, No. 7 (168),
pp. 101-115.
24. Clancey W.J. The epistemology of a rule based system – a framework for explanation, Artificial Intelligence, 1983, Vol. 20, pp. 215-251.
25. Owrang O.M.M. Database systems techniques and tools in automatic knowledge acquisition for rule-based expert systems, Knowledge-Based Systems, 2000, Vol. 1, pp. 201-248.

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