The solution of the applied task of constructing intelligent agents (IA) of action planning is proposed. The mathematical support of functioning of intellectual agents of action planning on the basis of ontologies is developed, which made it possible to formalize the behavior of such agents in the state space. The use of ontologies allows narrowing the search space for path from the initial state to the target state, rejecting irrelevant alternatives. A method of narrowing the search area for optimal IA activity is proposed. To assess the reaction of the environment on the behaviour of the IA a method based on reinforcement learning is developed. The two-criterion optimization problem of dynamic programming is formulated, which is solved by one of the iterative methods – by principal component analysis or by the multiple criterion method, depending on the possibility to numerically estimate the target functions of this optimization problem. The architecture of the system of planning the actions of specialized intelligence agents is proposed. It consists of an ontology that contains ontology of tasks, the solution of which is aimed at the functioning of a specialized IA, and a domain ontology, which sets out alternatives to solving individual subtasks. On the example of the problem of corrosion protection of the water supply or gas pipeline pipe the efficiency of the proposed approach is investigated. The software for the functioning of intelligent action planning agents based on constructed models, methods and algorithms has been developed, which make it possible to implement the individual components and functional modules of intellectual action planning agents on the basis of ontologies.

1. Gruber, T. A translation approach to portable ontologies. Knowledge Acquisition. 1993; 5 (2), 199-220. https://doi.org/10.1006/knac.1993.1008

2. Guarino, N. Formal Ontology, Conceptual Analysis and Knowledge Representation. Int. J. Human-Computer Studies. 1995; 43 (5-6), 625-640. https://doi.org/10.1006/ijhc.1995.1066

3. Sowa, J. Conceptual Graphs as a universal knowledge representation / Ed.: F. Lehmann. Semantic Networks in Artificial Intelligence. Spec. Issue of An Int. J. Computers & Mathematics with Applications. 1992; 23(2-5), 75-95. https://doi.org/10.1016/0898-1221(92)90137-7

4. Montes-y-Gómez, M.; Gelbukh, A.; López-López, A. Comparison of Conceptual Graphs. Lecture Notes in Artificial Intelligence. 2000; 1793. - http://ccc.inaoep.mx/~mmontesg/publicaciones/2000/ComparisonCG. https://doi.org/10.1007/10720076_50

5. Lytvyn, V.V. Knowledge bases of intellectual decision support systems. Lviv Polytechnic Publishing House: Lviv, 2011; p 240. (in Ukrainian)

6. Lytvyn, V.; Oborska, O.; Vovnjanka, R. Approach to decision support intelligent systems development based on ontologies. Econtechmod. 2015; 4, 4, 29-35.

7. Lytvyn, V.V.; Vovnyanka, R.V. The method of using ontologies in the OODA loop on the example of the functioning of higher education institutions. Complex systems and processes. 2012; 2, 38-43. (in Ukrainian)

8. Sutton, R. S.; Barto, A. G. Reinforcement Learning: An Introduction. A Bradford Book The MIT Press Cambridge: London, 2012; p 320.

9. Van Otterlo, M.; Wiering, M. Reinforcement learning and markov decision processes. Springer: Berlin Heidelberg, 2012; pp 3-42. https://doi.org/10.1007/978-3-642-27645-3_1

10. Lytvyn, V.V.; Oborska, O.V.; Vovnyanka, R.V. A method for modeling the decision support process in a competitive environment. Mathematical Machines and Systems. 2014; 1, 50-57. (in Ukrainian)

11. Oborska, O.V.; Vovnyanka, R.V. Modeling the behavior of a rational agent based on stimulating learning. Information systems and networks. 2014; 805, 61-69. (in Ukrainian)

12. Vovnyanka, R.V.; Dosyn, D.H.; Kovalevych, V.V. Method of extracting knowledge from text documents. Information systems and networks. 2014; 783, 302-312. (in Ukrainian)

13. Lytvyn, V.V.; Vovnyanka, R.V.; Dosyn D.H. Computer-aided building system CROCUS Basic Ontology. Electrical and Computer Systems. 2014; 13, 135-143. (in Ukrainian)

14. Serednytskyi, Ya.; Banakhevych, Yu.; Dragilev, A. Modern anti-corrosion insulation in pipeline transport. Part 3. Spline Ltd.: Lviv, 2005; p 286. (in Ukrainian)