Institute of Information Theory and Automation

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Department of Adaptive Systems

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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.

admin: 2018-06-04 14:32

Department detail

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Ing. Lubomír Bakule CSc.
Ing. Alkomiet Belal
Ing. Květoslav Belda Ph.D.
Ing. Milan Berka
Ing. Josef Böhm CSc.
Ing. Jindřich Bůcha CSc.
Ing. Kamil Dedecius Ph.D.
Dr. Siavash Fakhimi Derakhshan Ph.D.
Ing. Tatiana Valentine Guy Ph.D.
Mgr. Kateřina Henclová (Márová)
Ing. Jitka Homolová Ph.D.
RNDr. Ladislav Jirsa Ph.D.
Ing. Miroslav Kárný DrSc.
Ing. Václav Kůs Ph.D.
Ing. Petr Nedoma CSc.
Ing. Milan Papež
Ing. Lenka Pavelková Ph.D.
Ing. Petr Pecha CSc.
Anthony Paul Quinn
Prof. Oleksandr Rezunenko Ph.D.
RNDr. Vladimíra Sečkárová Ph.D.
Dominika Smolková
Ing. Vít Škvára
Doc. Ing. Václav Šmídl Ph.D.
Ing. Jakub Štěch
Ing. Ondřej Tichý Ph.D.
Ing. Lukáš Ulrych
Ing. Petr Zagalak CSc.
Duration: 2009 - 2012
The project aims to develop a novel on-line estimator of the key process variable in rolling mills by mixing multiple models with different sensitivities to inaccuracy in process data. The approach relies on the systematic treatment of uncertainty and merging of all available information.
Duration: 2009 - 2013
This long-term applied project covers various research and development activities according to specification of the industrial partner.
Duration: 2008 - 2011
Dynamic decision making (DM) maps knowledge into DM strategy, which ensures reaching DM aims under given constraints. Under general conditions, Bayesian DM, minimizing expected loss over admissible strategies, has to be used.
Duration: 2008 - 2010
Stochastic decentralized control of distributed systems is studied from theoretical and algorithmic point of view. Decentralization is formalized by imposing conditional independence assumptions in the centralized control problem. However, local models and aims are in general incompatible with this structure and a suitable projections must be found.
Duration: 2007 - 2008
The proposed project deals with modeling and simulation of dynamic value networks, which are increasingly replacing the traditional vertically integrated enterprises.