Institute of Information Theory and Automation

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Adaptive Systems - list of projects

Department: AS Duration: 2024 - 2026 Grantor: GACR
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.
Department: AS Duration: 2022 - 2026 Grantor: FG
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.
Department: AS Duration: 2020 - 2022 Grantor: GACR
Blind inverse problems (i.e. inverse problems with unknown parameters of the forward model) are well studied for models with uniform grids, such as blind image deconvolution or blind signal separation. Recently, new methods of learning of non-linear problems with differentiable nonlinearities (i.e.
Department: AS Duration: 2018 - 2021 Grantor: MSMT
The proposed project aims to contribute to theoretical and algorithmic development of cooperation and negotiation aspects while respecting agent imperfection and deliberation. The targeted solution should be applicable to decentralised dynamic DM under complexity and uncertainty. It will support a single agent acting within a network of strategically interacting agents.
Department: AS Duration: 2018 - 2020 Grantor: GACR
Optimal processing of distributed knowledge is key agenda in machine learning, signal processing and control, driven by sensor networks for smart environments, autonomous agents and distributed infrastruktures (clouds, Internet) serving the tnternet of things. Nodes may communicate via partially specied probability distributions (moments, etc.).
Department: AS Duration: 2018 - 2020 Grantor: GACR
Anomaly detection, which aims to identity samples very different from majority, is an important tool of unsupervised data analysis. Currently, most methods for anomaly detection use relatively simple shallow models without any complex layers and hierarchies.
Department: AS Duration: 2017 - 2021 Grantor: FG
Objective of the project is to contribute to theoretical and algorithmic development of cooperation and negotiation under complexity and uncertainty. The desired theory should be applicable to decentralised dynamic decision making under a flat cooperation structure without pre-coordination. It will support single agent acting within a network of strategically interacting agents.
Department: AS Duration: 2017 - 2018 Grantor:
Linear and bilinear models arise in many research areas including statistics, signal processing, machine learning, approximation theory, or image analysis.
Department: AS Duration: 2016 - 2018 Grantor: GACR
Rapid development of information and computer technology as well as availability of multiple, very frequently incompatible, informational sources have caused that decision makers (both humans and devices) are overloaded with information. Their imperfectness (i.e.
Department: AS Duration: 2016 - 2018 Grantor: GACR
Systems of partial differential equations (PDE) and ordinary differential equations (ODE) are studied from the point of view of Dynamical systems methods. Many problems from biology, chemistry, mechanics, control, information transmission, economics and other fields are changing in time and so they can be described by different types of Dynamical systems.
Department: AS Duration: 2014 - 2017 Grantor: MSMT
This proposal brings together experts in information theory with experts in atmospheric dispersion modelling, to tackle a particularly difficult and highly relevant scientific problem.
Department: AS Duration: 2014 - 2016 Grantor: GACR
The project aims to develop a dynamic distributed estimation framework, intended for fully distributed low-cost parameter estimation of stationary signals and reduced-complexity tracking of nonstationary processes.
Department: AS Duration: 2013 - 2016 Grantor: GACR
Department: AS Duration: 2013 - 2016 Grantor: GACR
Decision making (DM) is a targeted choice of actions based on given knowledge and preferences. Normatively, Bayesian DM, maximising expected utility, should be used under uncertainty but this happens less than desirable. Often, imperfection of the DM participant can be blamed as it limits the deliberation effort spent.
2018-08-02 13:59