Hierarchical Population Game Models of Machine Learning in Control Problems Under Conflict and Uncertainty

Artificial Neural Networks Multicriteria Training Based on Graphics Processors

“…To solve problem ( 1), ( 2), the hierarchical evolutionary algorithm (HEA) of the MCOU developed in [6,7] is used. As studies [5,6] show, the HEA MCOU, when used in ANN training tasks, shows a high computational complexity.…”

“…To solve problem ( 1), ( 2), the hierarchical evolutionary algorithm (HEA) of the MCOU developed in [6,7] is used. As studies [5,6] show, the HEA MCOU, when used in ANN training tasks, shows a high computational complexity. Therefore, it is proposed to implement the HEA software for solving problem (1), ( 2), based on the GPU architecture and OpenCL technology.…”

“…In this context, it is expedient to formalize the task of training an artificial neural network (ANN) in the form of a multicriteria optimization problem under uncertainty (MCOU). In [6,7], a coevolutionary technology for solving the MCOU problem was developed, which, as the results of computational experiments show, has an extremely high computational complexity. In [8][9][10][11][12], it was shown that a promising area of research is the parallel implementation of computing technology based on graphics processors (GPUs).…”

A Hierarchical Model of a Vector Nash Equilibrium Search in a Control Problem under Conflict and Uncertainty

“…The technology of the neuroevolutionary synthesis of algorithms for multi-object multicriteria systems' (MMS) control under conflict and uncertainty is based on the development and widespread use of coevolutionary algorithms for finding a game's stable-effective compromises (STEC) [1][2][3] and coordinated STEC (COSTEC) [4][5][6]. However, these coevolutionary algorithms have extremely high computational complexity.…”

Neuro-Evolutionary Synthesis of Game Models of Control under Uncertainty Based on Distributed Computing Technology

“…In [19][20][21], an evolutionary computational technology is being developed that provides the possibility of combining various game-theoretic principles of optimality and takes into account various uncertain factors on a single conceptual and algorithmic basis in the task of MMS control optimization under conflict and uncertainty. This technology is implemented in the form of a library of evolutionary algorithms [20] and has been used to solve practical problems of the evolutionary synthesis of neuro-game algorithms of MMS control in real time based on STECU [22][23][24]. The analysis of the results allows us to draw the following main conclusions: the neuro-evolutionary technology of multicriteria control algorithms under conflict and uncertainty synthesis is effective; at the same time, the algorithms of game MMS control models neuro-evolutionary synthesis have extremely high computational complexity; the practical use of neuro-evolutionary technology for solving problems of the specified class requires its implementation on high-performance distributed computing architectures.…”