The department since long has been orienting on research of complex systems with emphasis on discrete and event systems. Great results were achieved in the area of formalization of problems, modelling and simulation of the systems using formalisms of Petri Nets and state-charts. Distinguished results were achieved in the domain of agents and multi-agents technology. The research is oriented on coordination and cooperation of mobile agents, application of artificial intelligence methods, distributed intelligence and distributed control. The goal is to research and evolve methods of collective intelligence and apply the results in the area of swarm robotics, social systems, economical and political systems, etc.
- Multi-agent systems – research has been focussed on the design and development of agents architectures, design and development of agent’s coalition creation strategies and modelling of social behaviour of agents.
- Petri net-based modelling, analysis and supervisory control of agents in a group of agents and/or groups of agents in MAS and their simulation in the MATLAB environment and in special graphical simulators based on Petri nets. Cooperation of agents and/or groups of agents by means of the Petri net-based supervision with the aim to achieve prescribed goal of the global system. Modelling workflow by means of Petri nets.
- Complex systems control – emphasis on reasoning, learning and reliable action control with orientation on groups of mobile agents (e.g. mobile robots).
- Knowledge modelling and sharing – research has been focussed on ontological approach with the aim to support interoperability and integration of networked enterprises. Ontology knowledge representation for supporting agents’ coordination and agents’ coalition creation in large scale multi-agent systems has been researched as well.
- Manufacturing scheduling optimization by applying non-conventional artificial intelligence methods – in line with the newest trends in the control theory, the research has been focussed on biologically motivated methods. These methods abstract some of biological systems features in order to develop such interpretation of the nature behaviour that can successfully solve the problem.
Application of accomplished results are in different areas – robotic systems, manufacturing systems of continuous, discrete and hybrid character, crisis situation, transport systems.