L.S. Kuravsky - Dr.Sc. (Eng.), Professor, Dean of Computer Science Faculty, Moscow State University of Psychology and Education. E-mail: l.s.kuravsky@gmail.com S.I. Popkov - Post-graduate Student, Computer Science Faculty, Moscow State University of Psychology and Education. E-mail: rslw25@gmail.com

Problems of collective behavior and group management attracted the attention of researchers more than half a century. In recent years their importance has increased significantly due to topical interest to such problems as management of a robot team, flight control and mission accomplishment of groups of unmanned aerial vehicles and other mobile systems. The problems of managing groups of mobile objects, which have to coordinate their behavior in space and to cooperate to achieve a given result, are especially difficult. A sufficiently developed mathematical tool that could be acceptable for behavior control of the system agents in practice has not been fully established by the present time. This work presents an attempt to create the necessary mathematical grounds for a particular class of multi-agent sys-tems, the practical application of which is obvious and needs no comment.The probabilistic model to represent the behavior of an applied multi-agent system that introduces the game interaction between a set of agents and a target has been developed. The agent-s behavior is non-deterministic and therefore unpredictable from the target viewpoint. The system allows both coordinated and autonomous agent-s behavior that depends on availability of information about the presence and position of workable agents for each other. Agent-s behavior is determined with the aid of the algorithm that includes identification of the probabilistic model parameters using maximized objective functions representing individual and group probabilities for target defeating. Both the model and algorithm ensure the behavior control for relevant applied multi-agent systems.

 

1. Gorodeckijj V.I., Karsaev O.V., Samojjlov V.V., Serebrjakov S.V. Prikladnye mnogoagentnye sistemy gruppovogo upravlenija // Iskusstvennyjj intellekt i prinjatie reshenijj. 2009. № 2. S. 3-24.
2. Kuravskijj L.S., Marmaljuk P.A., Alkhimov V.I., JUrev G.A. Matematicheskie osnovy novogo podkhoda k postroeniju procedur testirovanija // EHksperimentalnaja psikhologija, 2012. T. 5. № 4. S. 75-98.
3. Kuravskijj L.S., JUrev G.A. Adaptivnoe testirovanie kak markovskijj process: modeli i ikh identifikacija // Nejjrokompjutery: razrabotka i primenenie. 2011. № 2. S. 21-29.
4. Markovskie modeli v zadachakh diagnostiki i prognozirovanija: Ucheb. posobie / Pod red. L.S. Kuravskogo. M.: RUSAVIA. 2013. 172 s.
5. Ovcharov L.A. Prikladnye zadachi teorii massovogo obsluzhivanija. M.: Mashinostroenie. 1969. 324 s.
6. Osipov G.S. Dinamicheskie intellektualnye sistemy // Iskusstvennyjj intellekt i prinjatie reshenijj. 2008. № 1. C. 47-54.
7. Osipov G.S. Metody iskusstvennogo intellekta. M.: Fizmatlit. 2011. 296 s.
8. Rutkovskaja D., Pilinskijj M., Rutkovskijj L. Nejjronnye seti, geneticheskie algoritmy i nechetkie sistemy. M.: Gorjachaja linija-Telekom. 2013. 384 s.
9. Fon Nejjman Dzh. Teorija samovosproizvodjashhikhsja avtomatov. M.: URSS. 2010. 384 s.
10. Cetlin M.L. Issledovanija po teorii avtomatov i modelirovaniju biologicheskikh sistem. M: Nauka. 1969. 316 s.
11. Kuravsky L.S., Marmalyuk P.A., Baranov S.N., Alkhimov V.I., Yuryev G.A., Artyukhina S.V. A New Technique for Testing Professional Skills and Competencies and Examples of its Practical Applications // Applied Mathematical Sciences. 2015. V. 9. № 21. R. 1003-1026. http://dx.doi.org/10.12988/ams.2015.411899.
12. Kuravsky L.S., Marmalyuk P.A., Yuryev G.A., Dumin P.N. A Numerical Technique for the Identification of Discrete-State Continuous-Time Markov Models // Applied Mathematical Sciences. 2015. V. 9. № 8. R. 379-391. http://dx.doi.org/10.12988/ams.2015.410882.
13. Kuravsky L.S., Marmalyuk P.A., Yuryev G.A., Belyaeva O.B., Prokopieva O.Yu. Mathematical Foundations of Flight Crew Diagnostics Based on Videooculography Data // Applied Mathematical Sciences. 2016. V. 10. № 30. R. 1449-1466, http://dx.doi.org/10.12988/ams.2016.6122.