Department of Adaptive Systems

Our research focuses on the following areas:

  • Adaptivity – The Department of Adaptive Systems designs decision-making systems that modify their behavior based on environmental changes, enhancing efficiency.
  • Broad Applicability – Adaptive systems are applied in diverse fields, including traffic control, technological management, nuclear medicine, financial data analysis, and electronic democracy.
  • Research on Complex Scenarios – The department is expanding adaptive systems to complex environments, focusing on decentralized control of large-scale systems and multi-participant normative decision-making.
  • Extensive Research Achievements – Decades of research have resulted in conceptual, theoretical, algorithmic, software, and application advancements in adaptive systems.
  • Bibliography of our Department

The Department of Adaptive Systems focuses predominantly on the design of decision-making systems, which modify their behavior according to the changing properties of their environment. This essential ability – adaptivity – enhances their efficiency. Decades of research have brought a number of conceptual, theoretical, algorithmic, software and application results. The applicability of adaptive systems is currently being extended toward complex scenarios by improving the classical adaptive systems and by developing their new versions.
The departmental “know-how” serves to resolve national as well as international research projects, running in collaboration with industry and government agencies. The interplay between theory and limited computing power is the common issue linking the various project domains. They include traffic control, management and control of technological systems, radiation protection, nuclear medicine, analysis of financial data, electronic democracy, etc. The increasing complexity of the problems addressed directs the main stream of the research toward decentralized control of large-scale systems and normative decision-making with multiple participants.

Our current projects

Advanced Bayesian methods for estimation of atmospheric pollutant sources

GA24-10400SS
2024-01-01 - 2026-12-31
Quantification of sources of atmospheric pollutants is crucial for regulatory purposes as well as for atmospheric science in general. Due to many physical limitations in observation and modeling, the existing methodologies have many simplifying assumptions, e.g. linear observation model or uncorrelated emission values, which cause inevitable bias in pollutant estimates. We propose to analyze and relax these assumptions to obtain more reliable pollutant estimates. We have already shown that the use of the Bayesian approach can reveal the temporal profile of pollutants as well as quantify associated uncertainty. Our main objective is to extend the Bayesian methodology to spatial-temporal pollutant emissions estimation implying new challenges such as ambiguity of pollutant origin or correlations between species or locations. Key application areas will be emissions of microplastics and microfibers (thanks to our long-term cooperation with the Norwegian institute for air research), volcanic emissions, ammonia emissions, and recent point-source atmospheric radionuclide releases.

Dynamic distributed decision making: role of uncertainty

EU-COST Action CA21169
2022-09-19 - 2026-09-18
The aim of the project is to promote understanding of complex interactions and the dynamics of decision making (DM) under complexity and uncertainty. The theory under consideration should be applicable to dynamic DM and interaction within a flat structure without any coordination. It will support modelling a living agent acting within a complex network of interacting heterogeneous agents.

Contact

  • Pod Vodárenskou věží 4, Prague 8, Czechia
  • guy@utia.cas.cz
  • +420 266 052 254