Skip to main content

Homepage of AS department


Department characteristics

Adaptive systems (AS) are systems making decisions or selecting control actions and concurrently improving themselves. They work under incomplete knowledge in uncertain, stochastic and dynamically changing environment. Traditionally, AS comprise adaptive estimators, detectors, predictors, controllers, etc. Design and application of AS represent long-term challenge that can be addressed only when using variety of disciplines labeled as cybernetics.

The list of people in our department indicates that the group is well balanced, covering the art of adaptive systems from theoretical, algorithmic and software aspects up to real-life applications. The group has been dealing with adaptive (control) systems and related problems more than 40 years. Through these years it has created a unified, theoretically and algorithmically well grounded approach to solving problems met in the area. The approach which can be labeled as Bayesian dynamic decision making is now perceived as Prague school of adaptive systems.

Theory and algorithms

The distinguished features of the department are:

  • the research activities aim at creating a unified theory;
  • the decision-making problem is solved as the technical one in its maximal possible completeness;
  • the constructive approach dominates the work (the best possible solution is searched for: improvements are rarely started from the analytical side);


Applications the Department is dealing with are a source of vital feedback that directs us to real, not just 'academical' questions. They ranges from adaptive control of technological processing up to advising to human beings managing complex process in industry, economy and medicine. The energy spent on gradual building of generic algorithmic and software tools starts to pay back so that we are able to enter new application domains very efficiently.

History of AS department

AS department was created in middle of sixties of the past century. Control applications based on physical modeling reached soon barrier that stems from complexity of the constructed models and impossibility to find feasible controllers to them. It was found that simple black-box models are often sufficient for design of efficient controllers. The need to learn model structure and its parameters stimulated interest in so called experimental identification. Search for an adequate methodology gradually singled out Bayesian methodology as the only known systematic tools suitable for solving the addressed class of problems. Gradually, following the improvements of the theoretical, algorithmic and evaluation tools, the interests have shifted to multivariate, non-linear and non-Gaussian cases. Also, control of basic level of technological processes has been gradually substituted by  higher level control and other application domains (physics, medicine, economy, societal decision making etc.). Attempt to created applicable generic tools and struggle with curse of dimensionality has become the main driving forces of the research we perform.

During decades of research a lot of people and partners contributed to our current know how, see the alumni list and list of honorary members. It is also worthwhile  to scan workshops and seminars we organized: they clearly demonstrate both paradigm shift we underwent including circles we return back to old ideas and old problems.  The  list of people actively working within the department, the recent seminars and addressed research as well as application topics indicate that the department is flourishing and contributes to progress of the field.

Submitted by admin on