The research in the domain of autonomous intelligent agent is the foreground of the introduction of artificial intelligence solution in all areas of economy. The intelligent autonomous systems combine the usage of pattern recognition, reasoning, decision making, conceptual modeling techniques and methods. The important part of intelligent agent implementation is to find the conceptualization which is suitable to the current problematic situation. Despite all progress around autonomous intelligent agents, humans are much more flexible and creative in making the right conceptualizations. They seamlessly adapt to the situation at hand and filter out all irrelevant details, using multiple perspectives and representations for the same objects. This research makes assumptions that every intelligent agent dynamically creates its own ontology used to interpret local knowledge. The mappings are established with this local ontology and the ontologies of other agents when needed, in order to share and reuse knowledge. In the article a formal model of problematic situation in the context of decision-making operation is presented. Models used in decision making and their relationships are described. In the second part of the article we analyze the process of conceptualization selection and arrive to the conclusion that this selection is done on multiple levels, starting from selecting the communicating agent with relevant domain of expertise, selecting and aligning ontologies of agents, selecting patterns and patterns languages which better correspond to the situation and lastly, selecting the relevant interpretations of concepts and relationships. In the last part of article, the problem of the selection of relevant knowledge provider is solved, using modified TOPSIS method. The proposed approach and directions of research will help to add flexibility to conceptual modeling of problematic situations by intelligent agents.
- Autonomous Vehicle Market Size, Share, Trends, Report 2023–2032. https://www.precedenceresearch.com/ autonomous-vehicle-market
- Katrenko A, Pasitchnyk V. (2013). Decision making: theory and practice (In Ukranian) Lviv, Novyj Svit-2000. 749.
- Dıaz A., Motz R., & Rohrer E. (2011). Making ontology relationships explicit in a ontology network. AMW,
- Rahman H., & Hussain M. I. (2020). A comprehensive survey on semantic interoperability for Internet of Things: State of the art and research challenges. Transactions on Emerging Telecommunications Technologies, 31(12), e3902.
- Klein M. (2001, August). Combining and relating ontologies: an analysis of problems and solutions. In OIS@ IJCAI.
- Bhatt M., Rahayu W., Soni S. P., & Wouters C. (2007). OntoMove: A Knowledge Based Framework for Semantic Requirement Profiling and Resource Acquisition. Australian Software Engineering Conference, 137–146. https://doi.org/10.1109/aswec.2007.36
- Mitra P., & Wiederhold G. (2004). An ontology-composition algebra. Handbook on Ontologies, 93–113. https://doi.org/10.1007/978-3-540-24750-0_5
- Euzenat J. (2008). Algebras of ontology alignment relations. In The Semantic Web-ISWC 2008: 7th International Semantic Web Conference, ISWC 2008, Karlsruhe, Germany, October 26–30, 2008. Proceedings, 7, 387–402. Springer Berlin Heidelberg.
- Savić M., Ivanović M., Jain L. C., Savić M., Ivanović M., & Jain L. C. (2019). Analysis of Ontology Networks. 148, 143–175.
- Bonacin R., Calado I., & dos Reis J. C. (2018). A Metamodel for Supporting Interoperability in Heterogeneous Ontology Networks (K. Liu, K. Nakata, W. Li, & C. Baranauskas, Eds.; Vol. 527, 187–196). Springer International Publishing. https://doi.org/10.1007/978-3-319-94541-5_19.
- Zavadskas E. K., Mardani A., Turskis Z., Jusoh A., & Nor K. M. (2016). Development of TOPSIS method to solve complicated decision-making problems – An overview on developments from 2000 to 2015. International Journal of Information Technology & Decision Making, 15(03), 645–682.