Institute of Information Theory and Automation

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PhD. Topic: Theory and algorithms for probabilistic support of operators (Kárný)

Type of Work: 
ÚTIA AV ČR, v.v.i., oddělení AS, 266052274
Adaptivní systémy, poradní systémy, bayesovské učení, pravděpodobnostní návrh

Hierarchical control and decision making connected with complex processes always contains a layer in which, decsions are made by human being, by an "operator".  The proposed topic is related to a group of projects, which aims to create an advanced computer support of such decsion making. Existing original probabilistic theory already proved to efficient for this task. There is however a range of theoretical, algorithmic and software problems that remain to be solved in order to get widely applicable tool. This provides an interesting and useful area for research of 2-3 PhD students.


The topic suits to FJFI, FEL, FIT ČVUT, FAV ZČU and others.

[1] Kárný M., Böhm J., Guy T. V., Nedoma P.: Mixture-based adaptive probabilistic control. International Journal of Adaptive Control and Signal Processing, 17 (2003), 2, 119-132. [2]Kárný Miroslav, Böhm Josef, Guy Tatiana Valentine, Jirsa Ladislav, Nagy Ivan, Nedoma Petr, Tesař Ludvík : Optimized Bayesian Dynamic Advising: Theory and Algorithms, Springer, (London 2006) [3]Kárný Miroslav, Guy Tatiana Valentine: Fully probabilistic control design , Systems and Control Letters vol.55, 4 (2006), p. 259-265 [4] Quinn A., Kárný Miroslav, Guy Tatiana Valentine : Fully probabilistic design of hierarchical Bayesian models , Information Sciences vol.369, 1 (2016), p. 532-547

2022-09-15 10:19