Planning the Behavior of an Autonomous Flying Robot in a Space of Subtasks. Knowledge Representation Model

Kalyaev, A.V., Chernukhin, Yu.V., and Kalyaev, I.A., Odnorodnye upravlyayushchie struktury adaptivnykh robotov (Homogeneous Control Structures of Adaptive Robots), Moscow: Nauka, 1990.

Afanas’ev, V.N., Dinamicheskie sistemy upravleniya s nepolnoi informatsiei. Algoritmicheskoe konstruirovanie (Dynamic Control Systems with Incomplete Information: Algorithmic Design), Moscow: Editorial URSS, 2018.

Stuart, R. and Norvig, P., Artificial Intelligence: A Modern Approach, Pearson, 2020, 4th ed.

Shumskij, S.A., Mashinnyj intellekt. ocherki po teorii mashinnogo obucheniya i iskusstvennogo intellekta (Machine Intelligence. Essays on the Theory of Machine Learning and Artificial Intelligence), Moscow: RIOR, 2020.

Ford, M., Architects of Intelligence: The Truth about AI from the People Building It, Birmingham: Packt Publishing, 2018.

Jones, T.M., AI Application Programming, Charles River Media, 2005.

Kober, J. and Peters, J., Learning Motor Skills: From Algorithms to Robot Experiments, Springer Tracts in Advanced Robotics, vol. 97, Cham: Springer, 2014.  https://doi.org/10.1007/978-3-319-03194-1

Melekhin, V.B., Model of representation and acquisition of new knowledge by an autonomous intelligent robot based on the logic of conditionally dependent predicates, J. Comput. Syst. Sci. Int., 2019, vol. 58, no. 5, pp. 747–765.  https://doi.org/10.1134/S1064230719050101

Kelly, A., Mobile Robotics: Mathematics, Models, and Methods, New York: Cambridge Univ. Press, 2013.

Kilani, Y., Bsoul, M., Alsarhan, A., and Al-Khasawneh, A.A., Survey of the satisfiability-problems solving algorithms, Int. J. Adv. Intell. Paradigms, 2013, vol. 5, no. 3, pp. 233–256.  https://doi.org/10.1504/IJAIP.2013.056447

Varlamov, O.O. and Aladdin, D.V., On the use of mivar networks for intelligent planning of the behavior of robots in the state space, Izv. Kabardino-Balkarskogo Nauchn. Tsentra Ross. Akad. Nauk, 2018, no. 6-2, pp. 75–82.

Karpov, V.E., Karpova, I.P., and Kulinich, A.A., Sotsial’nye soobshchestva robotov (Social Communities of Robots), Moscow: Lenand, 2019.

Simon, D., Algorithms of Evolutionary Optimization: Biologically Inspired and Population-Based Approaches to Computer Intelligence, Hoboken, N.J.: John Wiley & Sons, 2013.

Callan, R., Essence of Neural Networks, Prentice Hall, 1998.

Melekhin, V.B. and Khachumov, M.V., Partitioning complex tasks into subtasks in the process of planning goal-seeking behavior of an integral unmanned aerial vehicle in the state space, Morskie Intell. Tekhnol., 2020, no. 3-1, pp. 237–244.  https://doi.org/10.37220/MIT.2020.49.3.032

Melekhin, V.B. and Khachumov, M.V., Planning the behavior of an integrated unmanned aerial vehicle based on the division of complex tasks into subtasks in the state space, Morskie Intell. Tekhnol., 2020, no. 3-1, pp. 245–252.  https://doi.org/10.37220/MIT.2020.49.3.033

Melekhin, V.B. and Khachumov, M.V., Planning the collective activities of autonomous mobile intelligent agents under uncertainty, Sci. Tech. Inf. Process., 2021, vol. 48, no. 6, pp. 493–501.  https://doi.org/10.3103/S0147688221060101

Melekhin, V.B. and Khachumov, M.V., Fuzzy semantic networks as a knowledge representation model of autonomous intelligent systems, Sci. Tech. Inf. Process., 2021, vol. 48, no. 5, pp. 333–341.  https://doi.org/10.3103/S0147688221050051

Melekhin, V.B. and Khachumov, M.V., Tools for self-organizing a group of autonomous unmanned aerial vehicles under uncertainty, Aviakosm. Priborostr., 2019, no. 2, pp. 44–51.

Bersnhtein, L.S. and Melekhin, V.B., Planning polyphasic behavior of self-organizing intelligent systems, J. Comput. Syst. Sci. Int., 2000, vol. 39, no. 5, pp. 808–811.