Intelligent decision making

The process of engineering analysis, especially its preprocessing stage, comprises some knowledge-based tasks which influence the quality of the results greatly, require considerable level of expertise from an engineer; the support for these tasks by the contemporary CAE systems is limited. Analysis of the knowledge and reasoning involved in solving these tasks shows that the appropriate support for them by an automated system can be implemented using case-based reasoning (CBR) technology and ontological knowledge representation model. In this paper the knowledge-based system for intelligent support of the preprocessing stage of engineering analysis in the contact mechanics domain is presented which employs the CBR mechanism. The knowledge representation model is formally represented by the OWL DL ontology. Case representation model, case retrieval and adaptation algorithms for this model and the automated system are described.

2008 (1)
  • Peter Wriggers, Marina Siplivaya, Irina Joukova, Roman Slivin (2008) Intelligent support of the preprocessing stage of engineering analysis using case-based reasoning. Engineering with Computers 24 (4) pp. 383–404. doi web bibtex
    • Abstract
2007 (2)
  • Peter Wriggers, Marina Siplivaya, Irina Joukova, Roman Slivin (aug 2007) Intelligent Support of Engineering Analysis Using Ontology and Case-based Reasoning. Eng. Appl. Artif. Intell. 20 (5) pp. 709–720. Pergamon Press, Inc.. Tarrytown, NY, USA. doi web bibtex
  • P. Wriggers, M. Siplivaya, I. Zhukova, A. Kapysh, A. Kultsov (2007) Integration of a case-based reasoning and an ontological knowledge base in the system of intelligent support of finite element analysis. Computer Assisted Mechanics and Engineering Sciences Vol. 14, No. 4 pp. 753–765. bibtex

Global aim: Conservation and development of the Elbe river floodplains in a state, that the claims of water management, flood protection, agriculture, fishery, nature conservation, tourism and navigation are answered at the best possible rate.

  • Concept of CBR-based decision making developed;
  • general schema of integration of CBR, QR and ontology;
  • modified CBR-cycle supported with QR and ontology;
  • ontological knowledge representation model proposed as well as case representation and case qualitative models;
  • jRamwass system developed.


2014 (1)
  • Peter Wriggers, Marina Kultsova, Alexander Kapysh, Anton Kultsov, Irina Zhukova (2014) Intelligent Decision Support System for River Floodplain Management. In Knowledge-Based Software Engineering: 11th Joint Conference, JCKBSE 2014, Volgograd, Russia, September 17-20, 2014. Proceedings. pp. 195–213. Springer International Publishing. Cham. doi web bibtex

A concept of intelligent support of decision making in waste management using knowledge-based approach was developed, which is a promising way to increase efficiency of waste management system in the cities. Analysis of the domain of waste management shows that the appropriate support of decision making can be implemented using contemporary technologies of artificial intelligent such as rule-based reasoning and ontology. In the paper a general scheme of the integration of this reasoning mechanism and ontology is suggested, as well the problems of domain knowledge representation are considered. Implementation of a prototype of intelligent decision support system in waste management using rule-based reasoning and ontology is described.

2016 (1)
  • M. Kultsova, R. Rudnev, A. Anikin, I. Zhukova (July 2016) An ontology-based approach to intelligent support of decision making in waste management. In 2016 7th International Conference on Information, Intelligence, Systems Applications (IISA). pp. 1-6. doi bibtex