## 1 Introduction
### Dialog Data Exchange in a Collective of Network Agents  
The distant verification procedure in a collective of autonomous network agents imposes the task to determine actual parameters of the complicated robotic device, which in its turn imitates dozens of basic human abilities [57]. Such a task is vast and differs from traditional system identification tasks [58] and model-checking methods in computing [59; 60; 61]. Here, the autonomous agent is supposed to have AI algorithms, own tasks, goals, beliefs, etc., and it does not require intensive transfer of comprehensive instructions. Here, the details of the agent's work or its positioning are typically not very interesting for the distant administrator, as they should be controlled by the autonomous robot itself. Control procedures for advanced robots are expected to be displaced to generalized parameters, including the approval of received licenses, agent's authenticity, checks of data integrity, space-coordinate confirmation, and the verification of passed routes and cargo parameters [26]. Nevertheless, one should use initial debugging, periodic tests, checks after learning and self-learning sessions, and upgrading and data restoration procedures [6; 7; 8]. Actual tools are the biometrics of human administrators and checks of such digital \"fingerprints\" of robots, such as parts' numbers, used random and one-time paoles, and the prehistory of earlier work performed [26]. However, human experts remain the final instance. As complicated errors and faults may need the sequence of data and instruction transfer sessions, one should imitate the human ability to hold dialog conversations, which are needed to design some formal language models with a large data capacity. The known adequate scheme of a formal dialog interaction between two agents was formalized, e.g., back in 1987, by [PERSON] and [PERSON], in their book [62]. Further research has included, e.g., such development as the first version of NesC software [63], which was intended for the Tiny OS system. This method was based on the adapted version of C programming language and provided parallel process in agents. Further reviews of language models can be seen in [64]. The modern trend in language systems is represented mainly by ChatGPT v. 3.5 [65]. But this language tool uses complicated software and scarcely can be easily adapted to verification tasks in mass microcontroller platforms intended for low-traffic interaction and adaptable to specific requirements of the individual user. For highly specialized agents, there is no point to install any too powerful neural networks designs, as the spectrum of items for \"conversations\" of robots seems to be limited. The typical basic set of dialog communications for mass agents is shown in Figure 2, where robotic agents from some allocated MASs are supposed to carry out work in a space scene of action, including various individual partner and rival robots, MASs, people, animals, buildings, vehicles, and other objects. All of them are supposed to be recognized by a computer vision system [13,14] or LIDAR [66,67]. Here, for the sake of simplicity, we do not consider dashcam videos, which are a useful tool for verification but need massive AI-based image process. The agent in the allocated MAS can interact with its administrator, which is used to support the distant work of the agent and should verify some of its parameters in a planned mode or during the correction of the robot's behavior. The agent is supposed to exchange all possible communication messages with the administrator (marked by red arrows) and to use limited data sets in dialogs with other agents of the same MAS (shown by yellow arrows). In the last case, messages can mainly include parameters of the scene and tasks. Also, the scene of action imposes the presence of mobile or static agents of corporative and state regulators [26], checking licenses, payments, and technical parameters of robots.