Method of functioning of intelligent agents, designed to solve action planning problems based on ontological approach
DOI:
https://doi.org/10.15587/1729-4061.2017.103630Keywords:
ontology, intelligent agent, natural language processing, concept, space of states, action planningAbstract
The problem of operation of intelligent agents of action planning with the use of ontological approach was studied. Operation of intelligent agents is possible based on the knowledge of the subject-area, in other words, the knowledge base is used. Ontologies became the standard of knowledge base. Therefore, there arises the problem of development of methods and means of operation of intelligent systems based on ontologies, in particular intelligent agents of action planning.
The method of functioning of intelligent agents of action planning based on ontologies was developed. For this purpose, weights of importance of concepts and relationships were introduced to the structure of ontology. These weights are used for finding a path in the space of states. The space of states itself is built by using the language of requests to ontology. Optimization problem, which assigns the rational behavior of an intelligent agent, is two-criterial. To solve it, we chose the method of the main component, if objective functions may be evaluated, or the method of complex criterion, if these functions are impossible to evaluate.
Dimensionality of the space of states depends on the completeness of the ontology, and behavior effectiveness of an intelligent agent depends on the relevance of ontology. With this aim, in the course of automated development of ontology, we developed a method for evaluation of reliability of information sources that are used for developing ontologies. As a result of the studies, it was found that this approach allows us to increase operational efficiency of intelligent agents, if the process of ontology development is relevant to the needs of a subject domain.
The developed approach may serve as a base for constructing a unified methodology for development of intelligent agents of action planning if ontology of a subject domain is the central component of this software complexReferences
- Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5 (2), 199–220. doi: 10.1006/knac.1993.1008
- Guarino, N. (1995). Formal ontology, conceptual analysis and knowledge representation. International Journal of Human-Computer Studies, 43 (5-6), 625–640. doi: 10.1006/ijhc.1995.1066
- Sowa, J. F. (1992). Conceptual graphs as a universal knowledge representation. Computers & Mathematics with Applications, 23 (2-5), 75–93. doi: 10.1016/0898-1221(92)90137-7
- Bulskov, H., Knappe, R., Andreasen, T. (2004). On Querying Ontologies and Databases. Lecture Notes in Computer Science, 191–202. doi: 10.1007/978-3-540-25957-2_16
- Cali, A., Gottlob, G., Pieris, A. (2010). Advanced processing for ontological queries. Proceedings of the VLDB Endowment, 3 (1-2), 554–565. doi: 10.14778/1920841.1920912
- Galopin, A., Bouaud, J., Pereira, S., Seroussi, B. (2015). An Ontology-Based Clinical Decision Support System for the Management of Patients with Multiple Chronic Disorders. Stud Health Technol Inform, 216, 275–279.
- Zhao, T. (2014). An Ontology-Based Decision Support System for Interventions based on Monitoring Medical Conditions on Patients in Hospital Wards. University of Agder, 125.
- Ugon, A., Sedki, K., Kotti, A., Seroussi, B., Philippe, C., Ganascia, J. G. et. al. (2016). Decision System Integrating Preferences to Support Sleep Staging. Studies in health technology and informatics, 228, 514–518.
- Rospocher, M., Serafini, L. (2013). An Ontological Framework for Decision Support. Lecture Notes in Computer Science, 239–254. doi: 10.1007/978-3-642-37996-3_16
- Rospocher, M., Serafini, L. (2012). Ontology-centric decision support. Proceedings of the International Conference on Semantic Technologies Meet Recommender Systems & Big Data (SeRSy'12), 919, 61–72.
- Lytvyn, V. V. (2011). Bazy znan' intelektual'nykh system pidtrymky pryynyattya rishen'. Lviv: Vydavnytstvo L'vivs'koyi politekhniky, 240.
- Sutton, R. S., Barto, A. G. (2012). Reinforcement Learning: An Introduction. Cambridge, Massachusetts, London, 334.
- Van Otterlo, M., Wiering, M. (2012). Reinforcement Learning and Markov Decision Processes. Reinforcement Learning, 3–42. doi: 10.1007/978-3-642-27645-3_1
- Lytvyn, V., Tsmots, O. (2013). The process of managerial decision making support within the early warning system. Actual Problems of Economics, 11 (149), 222–229.
- Chen, J., Dosyn, D., Lytvyn, V., Sachenko, A. (2016). Smart Data Integration by Goal Driven Ontology Learning. Advances in Intelligent Systems and Computing, 283–292. doi: 10.1007/978-3-319-47898-2_29
- Lytvyn, V., Dosyn, D., Smolarz, A. (2013). An ontology based intelligent diagnostic systems of steel corrosion protection. Elektronika: konstrukcje, technologie, zastosowania, 54 (8), 22–24.
- Wong, W., Liu, W., Bennamoun, M. (2012). Ontology learning from text. ACM Computing Surveys, 44 (4), 1–36. doi: 10.1145/2333112.2333115
- Lytvyn, V., Vysotska, V., Pukach, P., Bobyk, І., Pakholok, B. (2016). A method for constructing recruitment rules based on the analysis of a specialist's competences. Eastern-European Journal of Enterprise Technologies, 6 (2 (84)), 4–14. doi: 10.15587/1729-4061.2016.85454
- Montes-y-Gomez, M., Gelbukh, A., Lopez-Lopez, A. (2000). Comparison of Conceptual Graphs. MICAI 2000: Advances in Artificial Intelligence, 548–556. doi: 10.1007/10720076_50
- Lytvyn, V., Uhryn, D., Fityo, A. (2016). Modeling of territorial community formation as a graph partitioning problem. EasternEuropean Journal of Enterprise Technologies, 1 (4 (79)), 47–52. doi: 10.15587/1729-4061.2016.60848
- Lytvyn, V., Vysotska, V., Chyrun, L., Dosyn, D. (2016). Methods based on ontologies for information resources processing. LAP Lambert Academic Publishing. Saarbrücken, Germany, 324.
- Basyuk, T. (2015). The main reasons of attendance falling of internet resource. 2015 Xth International Scientific and Technical Conference “Computer Sciences and Information Technologies” (CSIT). doi: 10.1109/stc-csit.2015.7325440
- Burov, E. (2014). Complex ontology management using task models. International Journal of Knowledge-Based and Intelligent Engineering Systems, 18 (2), 111–120. doi: 10.3233/kes-140291
- Lytvyn, V., Vysotska, V., Veres, O., Rishnyak, I., Rishnyak, H. (2016). Classification Methods of Text Documents Using Ontology Based Approach. Advances in Intelligent Systems and Computing, 229–240. doi: 10.1007/978-3-319-45991-2_15
- Lytvyn, V., Pukach, P., Bobyk, І., Vysotska, V. (2016). The method of formation of the status of personality understanding based on the content analysis. Eastern-European Journal of Enterprise Technologies, 5 (2 (83)), 4–12. doi: 10.15587/1729-4061.2016.77174
- Lytvyn, V., Vysotska, V., Veres, O., Rishnyak, I., Rishnyak, H. (2016). Content linguistic analysis methods for textual documents classification. 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT). doi: 10.1109/stc-csit.2016.7589903
- Serednytskyy, Y., Banakhevych, Y., Drahilyev, A. (2005). Suchasna proty koroziyna izolyatsiya v truboprovidnomu transporti. Chep. 3. Lviv-Kyiv, 288.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2017 Vasyl Lytvyn, Victoria Vysotska, Petro Pukach, Miroslava Vovk, Dmytro Ugryn
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.