## 2 Materials and Methods
### Description of Intelligent Agent Interactions Based on BDI  
Numerous tools have been developed for implementing MASs, with the internal architecture of agents in the predominant majority of them being based on the BDI concept [15; 16; 17; 18]. In general, a BDI (Belief-Desire-Intention) agent is defined in terms of beliefs, desires, and intentions. Here, beliefs represent the agent's knowledge of the world, desires correspond to its objectives, and intentions denote the actions the agent deems necessary to execute. When implementing BDI, the agent's knowledge system is augmented with behavioral model components that refine its decision-making. Within the agent's architecture, concepts such as rational behavior, reasoning, goals, plans, and others may be formalized. BDI-based agents can reason and represent their thought process in a human-readable format, are capable of communicating with each other, and can plan their actions. However, such agents are not well-suited for tasks that require modeling emotions and making changes to communication norms. Additionally, learning is a challenging task for BDI agents. To overcome learning difficulties, agents can be implemented using a hybrid approach that combines BDI with other artificial intelligence methods, such as machine learning. While BDI agents have less capability to model emotions compared to other cognitive architectures, such as Kismet, they are sufficient to predict the impact of emotions on an agent's behavior. Making changes to communication norms in BDI agents is difficult, but these challenges can be addressed when developing the architecture of a multi-agent system. The BDI model does not reflect the biological and neurological foundations of thinking, which limits its application in modeling biological systems. However, this simplification makes it easier to use in modeling production systems--its abstraction allows not only describing the behavior of employees but also the behavior of departments and enterprises, which is necessary in the type of systems under consideration. Despite all its shortcomings, the BDI model is the most suitable for developing models of both social systems in general and production systems in particular. The agent's belief system can be predefined and updated during simulation. Desires may either be prescribed or emerge through interactions with the environment. Intentions are determined during modeling based on desires and beliefs [15; 16; 17; 18]. Designing a multi-agent system (MAS) for complex technical and socio-economic systems does not require the development of agent-specific belief systems, as established approaches can be employed. Similarly, there are no constraints on how desires are formed or how agents select actions to execute. The standard BDI model, in order to simplify the application of the model by standardizing agent capabilities, is proposed to be supplemented with a plan library. Plans here are sequences of actions to achieve specific goals in prescribed situations. This addition is not new--although plans are not defined in the standard BDI model, they are introduced in most architectures based on this concept. For example, plans are specified when implementing agents according to the PRS, JAM, dMARS, and InteRRaP architectures, and others, as well as when creating agents using standard frameworks and platforms for developing MASs, such as JaCaMo, Jadex, and others [19]. Adding plans to the BDI concept when creating a model for a production system simulation tool simplifies the development of models for specific enterprises and does not reduce the flexibility in choosing implementation tools for solutions. To define behavior, a set of plans must be generated to achieve desired outcomes, evaluated, and optimized while accounting for other agents' actions. The list of plans corresponding to a desire may be fixed or dynamically updated.