In the process of transition to Industry 4.0, the importance of applying cutting-edge technologies such as machine learning and artificial intelligence to replace human operators in industrial processes is explained by the need to automate industrial production processes. Replacing qualified human experts with artificial neural networks opens up a lot of possibilities for the implementation of new methods of industrial process automation. The problem of industrial process automation is quite complex because the decision-making process of the human expert is accompanied by uncertainty. Artificial neural networks represent one of the basic branches of artificial intelligence. At the moment, they are used in various fields to solve problems for which classical methods are unable to provide practical solutions. Thus, the problem of developing and training artificial neural networks for solving industrial process automation problems acquires major importance in the design of artificial intelligence systems. The training process directly depends on the data set on the basis of which the neural network is designed.

The paper presents the results of research carried out to solve complex problems aimed at the efficient use of natural and energy resources. The objectives of the paper are achieved by identifying the control process based on a Multi-Agent system with distributed data processing that implements a Multi-objective optimal solution search model based on the application of a Genetic Algorithm with Collective Computation. The set of Agents presents a computational architecture that forms a structured network topology based on a P-Systems model presented in the form of a Venn diagram. The Object diagram and the Venn diagram of the P-Systems model are presented in the paper. The correctness of the developed models was verified on the basis of a control system of the artificial lighting process that provides for the minimization of energy consumption, while ensuring the quality of the lighting process.

In this paper are presented the results of research done in the system projecting and research for making informed decisions based on the collaborative Agents. The mathematical model used in this research had the goal to find an optimal solution in a multi-objective space by using methods inspired by nature, especially evolutional calculus algorithms. The calculus system’s architecture consists of two sets of Agents: agents that deliver data and information, and Agents that consume it. The interconnection process between Agents is a dynamic one which evolves in time and it determines the topology of the calculus system.

The article analyses the implementation of the dual study model for higher education at the Technical University of Moldova. This goal was one of the basic ones within the Erasmus+ COOPERA project "Integrating Dual Higher Education in Moldova and Ukraine". The development vision and needs of the national economy define the leading arguments for the pilot programs at the Technical University of Moldova. The faculty team proposes a Dual higher education model appropriate for students from two engineering programs. The model specifies the roles of the student, university and company in dual education and the benefits of all involved actors. In the designing phase of the project, the teaching staff consult students, company administrators and specialists to fit all interests into one joint model and curricula.

The development and research of Smart City Service Systems is a very important area for the future of mankind. The urbanization process imposes new criteria for qualitative and quantitative assessment of population well-being, which will involve processing a very large volume of information, organizing the data exchange and processing. This paper proposes a Multi-Agent Smart City Services system based on Spatial-Temporal logic. In order to optimize the criteria for the qualitative and quantitative evaluation of services, the set of agents is divided into: the subset of agents that deliver services and the subset of service consumers agents. The system diagram, the synthesis of the agents, the operators of temporal and spatial logic was elaborated. The relationship between the subset of service delivery agents and the subset of agents of service consumers is determined by game theory models.