Methods of swarm algorithms solution of applied tasks in geoinformation systems

: pp. 87 - 106
Lviv Polytechnic National University
Chernivtsi Philosophical and Legal Lyceum № 2, Chernivtsi

At this article proposed to use intelligent planning agents using ontological approach to automate the procedures of formation of many alternative solutions and the choice of rational decision in branch of GIS. It proposed to use the developed knowledge base in the field of methods of swarm intelligence based on adaptive ontology and a database of scientific publications in this field. All applied problems in the branch of geoinformation systems are divided into classes of problems: stationary, quasi-stationary, dynamic. It is suggested to determine the free parameters for individual swarm algorithms based on machine learning with reinforcement, namely the Q-Learning method. On the basis of this method Markov chains for the swarm algorithms were constructed. Reinforcement consisted of the expert analysis of the results obtained by a certain swarm algorithm. On the example of territorial administration, optimal values of the parameters of individual swarm algorithms were found.

Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5 (2), 199-220.

Guarino, N. (1995). Formal ontology, conceptual analysis and knowledge representation. International Journal of Human-Computer Studies, 43 (5-6), 625-640.

Sowa, J. F. (1992). Conceptual graphs as a universal knowledge representation. Computers & Mathematics with Applications, 23 (2-5), 75-93.

Bulskov, H., & R. Knappe, T., & Andreasen, R. (2004). On Querying Ontologies and Databases. Lecture Notes in Computer Science, 191-202.

Cali, A., & G. Gottlob, A. & Pieris, A. (2010). Advanced processing for ontological queries. Proceedings of the VLDB Endowment. 3 (1-2), 554-565.

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, JG., & Garda, P., & Bouaud. J., & Pinna, A. (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.

Rospocher, M., & Serafini L. (2012). Ontology-centric decision support. Proceedings of the International Conferenceon Semantic Technologies Meet Recommender Systems & Big Data (SeRSy'12). 919. 61-72.

Wong, W., & Liu, W., & Bennamoun, M. (2012). Ontology learning from text. ACM Computing Surveys. 44 (4), 1-36.

Sutton, R., & Bartow, A. (1998). Reinforcement Learning: An Introduction. / MIT Press. 322.

Maes, F. (2009). Structured prediction with reinforcement learning. Machine Learning. 271-301.

Jiang, J. (2012). Learned Prioritization for Trading Off Accuracy and Speed. Inferning: Interactions between Inference and Learning. Divisions. Retrieved October 28, 2019, from

Sutton, R., & Bartow, A. (2014). Reinforcement learning. Laboratory of knowledge.42-96.