багатоагентна система

This paper presents a framework for automating software development security using a Security as Code approach enhanced with a multi-agent artificial intelligence system. The research addresses the limitations of traditional DevSecOps practices by deploying specialized AI agents to perform static code analysis, generate and enforce security policies, and monitor system behavior. The architecture integrates security throughout the CI/CD pipeline and runtime, enabling autonomous decision-making, adaptability to threats, and reduced developer overhead.

Досліджується проблема координації стратегій мультиагентних систем на основі моделі стохастичної гри з локальними зв’язками. Розроблено ігровий рекурентний метод та алгоритм формування узгоджених стратегій агентів в процесі мінімізації функцій середніх програшів. Виконано комп’ютерне моделювання стохастичної гри для побудови карти скоординованих стратегій. Досліджено вплив параметрів моделі на збіжність ігрового методу.

A method of multi-agent coordination with deferred asynchronous messaging in a distributed coordination space has been proposed. The method has been based on the concept of multi-agent conditional interaction. The method has used 1) a distributed coordination space in which agents move, 2) the rules of state transitions for the coordination space nodes depending on the movements of agents, 3) the rules of agents move and state transitions depending on the states of the coordination space nodes, 4) a multi-agent coordination game based on the coordination space and the rules.

The problem of coscheduling spatial self-organization control processes and distributed data collection processes in a multi-agent system has been considered. The goal of coscheduling is to find and use the possibilities of functional coordination of these processes and increase the efficiency of the multi-agent system due to their parallel execution. An analysis of the main features of spatial self-organization tasks that affect the solution of the problem of coscheduling has been carried out.

In this paper, a stochastic game model of self-organization of strategies of stochastic game of mobile agents in the form of cyclic behavioral patterns, which consist of coordinated strategies for moving agents in a limited discrete space, is developed. The behavioral pattern of a multi-agent system is a visualized form of orderly movement of agents that arises from their initial chaotic movement during the learning of a stochastic game.