Special Stream on Advanced Automatic Control and Information Technology

Scientists from Cracow University of Technology and Lviv Polytechnic National University organize this Stream

Today the information processes of management and control systems should be focused on information technology-oriented users, and the transition from procedural to descriptive models and evidence-based decision-making. It will allow users to interact effectively with information systems at all stages of their life cycle, getting support and explanations in solving their problems. The intelligent information system helps the person formulate and solve problems by integrating the problem solving tools with a known and unknown solution schemes, which may be built from an existing set of schemes or requires the creation of an entirely new scheme.

The development of information technology, artificial intelligence and automatics has created the conditions for the transition to adaptive technologies, in which the properties of mobility and flexibility are complemented by the ability to self-organize and self-development, which are provided by the mechanisms of generalization, classification, intellectual data mining and adaptation. To achieve this, adaptive systems need to demonstrate self-determination based on multi-level, structured and complex representations.

Effective implementation of the new concept is possible only in a comprehensive adaptive technology for the creation, implementation, operation and development of information systems, the application component that ensures full system functionality and the interaction of components using the following system tools:

• supporting the functioning of the application elements of the IT system based on the general model of the control and management objects and business processes and defining the methods of their implementation;
• design based on a multi-level approach, which allows the system to be "expanded" through an iterative-sequential implementation process of system description mappings, starting from the conceptual, user-oriented level, and ending with the implementation language level, oriented towards a qualified programmer;
• learning that gives technology the ability to self-development through finding and using patterns, adaptive behavior;
• data and knowledge management, which gives the user the ability to work with data about the control and management object, models at all stages of the IT system life cycle, and allows the redefinition of object classes in the functioning process(es).

In terms of implementing this approach, we will be happy to discuss in our stream new models, methods, technologies and tools for use in the above mentioned areas.

Automatic Control

Internet of things: models, methods for efficient IoT-generated data processing, enrichment, storage strategies for scalable and cost-effective data management, successful products, experience.

Intelligent control: methods of learning to deal with fuzzy and AI for real-time decision-making and pattern recognition, predictive analytics for system maintenance optimization, AI-based failure prediction

Control systems in conditions of uncertainty: models and methods of control in conditions of uncertainty of external influences, the dynamics and the parameters of the system. We will appreciate seeing the application and comparison of both classical methods, such as statistical, and artificial intelligence methods.

Information Technologies and Systems

Description, design, implementation and development of information systems: models, methods, modern architecture patterns, approaches, technologies, languages, tools, implementation experience.

Computer-aided information systems design technologies: modern agile-technologies of information systems design; automated design technologies of information systems architecture development

Traditional areas of artificial intelligence. We are waiting for models and methods in the traditional areas of artificial intelligence application: reasoning; prepresentation and processing of knowledge; machine learning; communication; inaccuracy and uncertainty; image recognition; self driving cars; data mining; planning of navigation for mobile robots; pattern processing and computer vision; digital conversion and signal processing in transport systems; intelligent computation (fuzzy logic, artificial neural networks, genetic algorithms and evolutionary modeling).

Modern areas of artificial intelligence. We are waiting for models and methods for the development of the most modern areas of artificial intelligence: Deep Learning and Optimization strategies, novel architectures to enhance performance and efficiency, Foundation Models and Self-Supervised Learning, Autonomous AI Agents, AI in Education, Ethical AI and AI Safety

Co-Chairs of the Stream

• Lukasz Scislo, Poland
• Zenoviy Veres, Ukraine

Members of IPC

• Janusz Gołdasz, Poland
• Zbigniew Kokosiński, Poland
• Adrian Nakonechnyy, Ukraine
• Volodymyr Samotyy, Poland
• Sergii Telenyk, Ukraine
• Ulyana Dzelendzyak, Ukraine
• Łukasz Jastrzębski , Poland
• Bogdan Korniyenko, Ukraine
• Michal Kubik, Czech Republic
• Leonid Moroz, Poland
• Oleksandra Mychuda, Ukraine
• Paweł Orkisz, Poland
• Robert Sałat, Poland
• Eduard Zharikov, Ukraine