This article explores the use of swarm intelligence algorithms in unmanned vehicles (UVs), focuses on their main advantages for improving the efficiency and productivity of systems. Unmanned vehicles, which can operate autonomously or under remote control, play a significant role in such areas as surveillance, search and rescue, agriculture and military operations.
This paper proposes a novel architecture of a multi-agent system and its formal specification for analyzing and adaptively correcting students' learning trajectories using software agents in digital learning environments. The proposed approach integrates artificial intelligence tools, tem- poral logic, and a multi-agent system architecture to ensure personalized adaptation of educational content.
The game model of multi-agent systems of self-organizing in the conditions of uncertainty is developed. The formulation of a stochastic game problem is carried out, criteria of self-organizing of strategies of players are defined, a recurrent method, algorithm and software of learning of multi-agent system to simulate the synchronised rhythmic luminescence of a colony of fireflies are developed.