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Design of intelligent decision support systems using ontological approach

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Języki publikacji
EN
Abstrakty
EN
Methods of developing and functioning of intelligent decision support systems based on precedents applying adaptive ontology that are part of intelligent agents are analyzed. Method of distance defi nition between precedens and current situation based on adaptive ontology is developed. Using mathematical tools of the dynamic programming for modelling of intelligent system functioning is considered. Simplifying the task model is proposed to weigh signs of ontology concepts. Examples of such problems for six processes concerning metal structures protection and maximizing their lifetime are presented.
Twórcy
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  • Department of Information Systems and Networks, Lviv Polytechnic National University
Bibliografia
  • 1. Aamodt A. and Plaza E. 1994. Case-based reasoning: Foundational issues, methodological variations, and system approaches AI Communications, Vol. 7, Issue 1, 39-59.
  • 2. Russel S., Norvig P. 2003. Artifi cial intelligence. A modern approach, Prentice-Hall, Upper Saddle River, N.J.
  • 3. Gruber T. R. 1993. A translation approach to portale ontologies Knowledge Acquisition. No. 5 (2), 199-220.
  • 4. Dosyn D., Darevych R., Lytvyn V. and Dalyk U. 2006. New knowledge evaluation using massage model of NLT document Proceedings of the International Conference on Computer Science and Information Technologies, 118-119.
  • 5. LytvynV., Shakhovska N., PasichnykV. and Dosyn D. 2012. Searching the Relevant Precedents in Dataspaces Based on Adaptive Ontology. Computational Problems of Electrical Engineering. – Volume 2, Number 1. – Lviv, 75-81.
  • 6. Dosyn D. and Lytvyn V. 2012. Planning of Intelligent Diagnostics Systems Based Domain Ontology The VIIIth International Conference Perspective Technologies and Methods in MEMS Design. - Polyana, Ukraine, 103.
  • 7. Lytvyn V., Dosyn D., Medykovskyj M. and Shakhovska N. 2011. Intelligent agent on the basis of adaptive ontologies construction Signal Modelling Control. – Lodz.
  • 8. Montes-y-Gómez M., Gelbukh A. and López-López A. 2000. Comparison of Conceptual Graphs Lecture Notes in Artificial Intelligence Vol. 1793. – Springer-Verlag: http://ccc.inaoep.mx/~mmontesg/publicaciones/ 2000/ComparisonCG.
  • 9. Sowa J. 1976. Conceptual graphs for a database inter face IBM Journal of Research and Development. – Vol. 20. – № 4, 336–357.
  • 10. Knappe R., Bulskov H. and Andreasen T. 2004. Perspectives on Ontology-based Querying International Journal of Intelligent Systems: http://akira.ruc.dk/~knappe/publications/ ijis2004.pdf
  • 11. Frederick S. and Gerald J., 2005. Introduction to Operations Research McGraw-Hill: Boston MA; 8th. (International) Edition.
  • 12. Callı A., Gottlob G., Pieris A. 2010. Advanced processing for ontological queries Proc. of the 36th Intl Conf. On Very Large Databases, 554–565.
  • 13. Booch G., Jacobson I. and Rumbaugh J. 2000. OMG Unified Modelling Language Specification.
  • 14. Gladun A. and Rogushina J. 2006. Ontological Approach to Domain Knowledge Representation for Informational Retrieval in Multiagent Systems International Jornal “Information Theories & Applications”. - V.13. - № 4, 354-362.
  • 15. Guarino N. 1995. Formal Ontology, Conceptual Analysis and Knowledge Representation International Journal of Human-Computer Studies. – № 43(5-6), 625–640.
  • 16. Boris M., Motik B., Sattler U. and Studer R. 2005. Query Answering for OWL-DL with Rules Journal of Web Semantics. – № 3(1), 41–60.
  • 17. Saleh M.E. 2011. Semantic- Based Query in Relational Database Using Ontology Canadian Journal on Data, Information and Knowledge Engineering. – Vol. 2. – № 1, 1–16.
  • 18. Dosyn D., Lytvyn V. and Yatsenko A. 2012. DP-optimization of steel corrosion protection techniques in the intelligent diagnostic system FHММ. - №9, 329-333.
  • 19. Shakhovska N., Lytvyn V. and Medykovskyj M. 2012. Dataspace Class Algebraic System for Modelling Integrated Processes Journal of applied computer science vol. 20 no. 1, 69-80.
  • 20. Dosyn D., Darevych R. and Lytvyn V. 2004. Modelling of the intelligent text recognition agents based on dynamic ontology Proceedings of the 4th International Conference „Internet – Education – Science – 2004”. – Baku-Vinnytsia- Veliko Turnovo. – V. 2, 577–579.
  • 21. Kuzmin O. Ye., Melnyk O. H., Shpak N. O., Mukan O.V. 2012. The concept of creation and use of the polycriterial diagnostics systems of enterprise activity, Econtechmod. An international quarterly journal on economics in technology, new technologies and modelling processes. Vol. I, No 4, 23-28.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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