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

Under consideration is the method of probabilistic forecasting of macro parameters, representing general patterns of an applied multi-agent system behavior. This system introduces the game interaction between a set of agents and a target. The behavior dynamics of such a system is described by the Markov random process with discrete states and time and represented in the terms which are considered convenient for interpretation and practical control of macro parameters. The corresponding calculationsare based on common characteristics of the initial conditions. The probabilistic model of behavior of the system is generalized for the case of the mobile target and supplemented with formulas for the dynamical calculation of probabilistic distributions of the target defeat by the agents as well as the agents defeat by the target.

1. Gorodeczkij V.I., Karsaev O.V., Samojlov V.V., Serebryakov S.V. Prikladny'e mnogoagentny'e sistemy' gruppovogo upravleniya // Iskusstvenny'j intellekt i prinyatie reshenij. 2009. № 2. S. 3–24. 
2. Kuravskij L.S., Popkov S.I. Veroyatnostnaya model' povedeniya prikladnoj mnogoagentnoj sistemy' // Nejrokomp'yutery': razrabotka i primenenie. 2016. № 9. S. 22–34. 
3. Kuravskij L.S., Jur'ev G.A. Adaptivnoe testirovanie kak markovskij proczess: modeli i ix identifikacziya // Nejrokomp'yutery': razrabotka i primenenie. 2011. № 2. S. 21–29. 
4. Markovskie modeli v zadachax diagnostiki i prognozirovaniya: Ucheb. posobie / Pod red. L.S. Kuravskogo. M.: RUSAVIA. 2013. 172 s. 
5. Osipov G.S. Dinamicheskie intellektual'ny'e sistemy' // Iskusstvenny'j intellekt i prinyatie reshenij. 2008. № 1. C. 47–54. 
6. Osipov G.S. Metody' iskusstvennogo intellekta. M.: Fizmatlit. 2011. 296 s. 
7. Aras R., Dutech A., Charpillet F. Cooperation through communication in decentralized Markov games. In International Conference on Advances in Intelligent Systems // Theory and Applications – AISTA’2004. Luxembourg-Kirchberg/Luxembourg. 2004. 
8. Boutilier C. Planning, learning and coordination in multiagent decision processes // In Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge, Morgan Kaufmann Publishers Inc. 1996. P. 195–210. 
9. Claus C., Boutilier C. The dynamics of reinforcement learning in cooperative multiagent systems // In Proceedings of the National Conference on Artificial Intelligence, John Wiley & Sons Ltd. 1998. P. 746–752. 
10. Kuravsky L.S., Popkov S.I., Artemenkov S.L. An applied multi-agent system within the framework of a player-centered probabilistic computer game // International Journal of Modeling, Simulation, and Scientific Computing. 2017. V. 8. № 4. https://doi.org/10.1142/S1793962317500635. 
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. 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. P. 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. P. 1449–1466. http://dx.doi.org/10.12988/ams.2016.6122. 
14. Miikkulainen R. Creating Intelligent Agents in Games. 2006. The Bridge: 5–13. 
15. Owen G. Game Theory. Academic Press. 1995. 
16. Read M., Möslinger Ch., Dipper T., Kengyel D., Hilder J., Thenius R., Tyrrell A., Timmis J., Schmickl T. Profiling Underwater Swarm Robotic Shoaling Performance using Simulation // In Proceedings of TAROS 2013. 2013. P. 456–462. 
17. Snodgrass S., Ontanon S. A hierarchical mdmc approach to 2d video game map generation // In Eleventh Artificial Intelligence and Interactive Digital Entertainment Conference. 2015.