## 4 Discussion  
Existing solutions for modeling organizational systems using MASs [8, 9, 10, 11, 12] offer a limited set of agent roles reflecting the types of production departments, but not the types of their employees and environmental agents. Overcoming this limitation by introducing an invariant ontology for managing complex production systems simplifies the development of models for specific enterprises.  
The developed foundational ontological model of multi-agent interactions in complex production systems is practical and easy to implement. It enables the design of multi-agent systems (MASs) for diverse complex production systems, as demonstrated using the example of resource management in the oil and gas industry. Refining the ontology in accordance with modern approaches to managing complex production systems and advancing the mathematical framework for defining rational agent behavior could enhance the performance of the decision support system based on the proposed model.  
Within this project, game-theoretic tools were selected for implementation. Complex production systems require tools capable of simulating not only agent actions but also their influence and interactions with the environment. Thus, game theory is widely applied in MAS development, as it provides a mathematical framework for analyzing interaction strategies among agents with divergent interests and objectives [23, 24, 25, 26, 27]. Furthermore, game theory is increasingly combined with machine learning methods, facilitating further model refinement due to its flexibility and integrability [23, 24, 25, 26, 27].  
Another key feature of the proposed model is the use of the BDI (Belief-Desire-Intention) concept for designing the internal architecture of intelligent agents. Literature describes various intelligent agent models such as Soar, ACT-R, ICARUS, BDI, subsumption architecture, CLARION [20], among which BDI and Soar are the most prominent. BDI [28] is straightforward to implement compared to Soar, a low-level architecture rooted in fundamental aspects of intelligence and was among the first models successfully commercialized in frameworks [29]. The BDI model has limitations, such as low capabilities for agent learning, modeling emotions, and making changes to communication norms [30]. These shortcomings can be compensated for by implementing the architecture of a cognitive agent, environment, and system organizational structure, including the selection of a cognitive architecture, framework, or platform for development. Despite the limited descriptive capabilities of the BDI model, it remains the most suitable choice for modeling production systems.  
The choice of BDI was also driven by the requirements for the software implementation of the multi-agent interaction model [30]. This work prioritizes flexible and scalable solutions that can be developed either from scratch using specialized libraries (MASON, Pade, NL4 Py, Repast4 Py) or frameworks (JADE, RePast, INGENIAS) or built upon dedicated platforms (JADE, Jadex, INGENIAS, JACK, MadKit, JaCaMo), most of which adopt the BDI architecture.  
Selecting optimal tools for the software implementation of the multi-agent interaction model in complex production systems is a focus of the project's next stages.