Lazy Fully Probabilistic Design: Application Potential

  • Tatiana V. Guy
  • Siavash Fakhimi DerakhshanEmail author
  • Jakub Štěch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10767)


The article addresses a lazy learning approach to fully probabilistic decision making when a decision maker (human or artificial) uses incomplete knowledge of environment and faces high computational limitations. The resulting lazy Fully Probabilistic Design (FPD) selects a decision strategy that moves a probabilistic description of the closed decision loop to a pre-specified ideal description. The lazy FPD uses currently observed data to find past closed-loop similar to the actual ideal model. The optimal decision rule of the closest model is then used in the current step. The effectiveness and capability of the proposed approach are manifested through example.


Lazy learning Fully Probabilistic Design Decision making Linear quadratic gaussian control 



The authors would like to thank Miroslav Kárný for valuable discussions and comments. The research has been partially supported by the Czech Science Foundation, project GA16-09848S.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Tatiana V. Guy
    • 1
  • Siavash Fakhimi Derakhshan
    • 1
    Email author
  • Jakub Štěch
    • 1
  1. 1.Department of Adaptive SystemsInstitute of Information Theory and Automation, The Czech Academy of SciencesPrague 8Czech Republic

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