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Adaptive systems are dynamic units that learn their environment while make their decisions. Within this broad framework, the main research areas of the department are:


Probabilistic Design: Fully Probabilistic Design of Dynamic Decision Strategies

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.

Advanced Control: Adaptive LQ Controllers and Predictive Controllers

Advanced control strategies based on LQ and predictive algorithms are significant for different industrial applications. The aim of the research is a fixing of control theory in this area and developing of complete computer-aided design of adaptive controllers. The design arises from raw data, measured on a real controlled system; user's knowledge; and user demands and it results into a completely pre-tuned and verified controller.

Stochastic Sampling: Sequential Sampling Methods for Identification and Control

Sampling methods are known for being computationally expensive, however recent research and increasing performance of computers improved applicability of these methods in such a way that they represent a strong alternative to traditional approximation methods.

Linear Systems: Advanced Theory of Linear Systems

Linear systems form a well-developed core of advanced controllers. Consequently, their understanding and even minor improvements have a deep impacts on the field. 

Traffic Control: Urban Traffic Feedback Control

The general objective of the project is the enrichment of the complete design line of LQG controllers so that it will cover steps related to state estimation, ideally with mixed-type (continuos and discrete) states.

Bayesian learning: Models with strictly bounded noise

This research deals with Bayesian learning using models with bounded noise. These model are suitable for a description of physically constrained quantities.

Investigated Research Topics:

Decision-Making: Adaptive Decision-Making under Informationaly Demanding Conditions

Knowledge extraction maps extensive data sets on lower dimensional objects. Its results always serve to a subsequent, often dynamic, decision making. Decision-making quality is substantially influenced by the mapping used. This simple fact is relatively rarely respected by many elements in the overwhelming arsenal of existing mappings. A complete solution of decision making problems that includes explicitly the discussed mapping are severely limited by computational complexity.

Advising: Optimized Bayesian Dynamic Advising

Complex technical and societal systems are often managed by human beings (operators, managers, medical doctors ...) who badly need help to reach high standards of their acting. Conceptual solution, formalization, algorithmization and implementation of such advising systems have been addressed. The resulting system is able to cope with dynamically changing incompletely known multi-attribute environment, to learn and optimize dynamic decision-making strategy realized either by human being or automatically.

Multi-Participant DM: Theory of Multi-Participant Bayesian Decision Making

Single decision-making unit like  the advising system or non-linear adaptive controller reach relatively soon its applicability barrier, mostly caused by computationally complexity and limited reliability. Then, a distributed solution is needed. The theory and algorithms covering design and cooperation  Bayesian decision-making units (participants) are inspected. They respect limited abilities of such units, incomplete knowledge and random nature of the surrounding environment. The problem is scientifically challenging with an extreme applicability width.
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