Počet záznamů: 1  

Lazy Fully Probabilistic Design of Decision Strategies

  1. 1.
    0434674 - ÚTIA 2015 RIV CH eng C - Konferenční příspěvek (zahraniční konf.)
    Kárný, Miroslav - Macek, Karel - Guy, Tatiana Valentine
    Lazy Fully Probabilistic Design of Decision Strategies.
    Advances in Neural Networks – ISNN 2014. Cham: Springer, 2014 - (Zhigang, Z.; Yangmin, L.; King, I.), s. 140-149. Lecture Notes in Computer Science, 8866, XVI. ISBN 978-3-319-12435-3.
    [11th International Symposium on Neural Networks, ISNN 2014. Hong Kong and Macao (CN), 28.11.2014-01.12.2014]
    Grant CEP: GA ČR GA13-13502S
    Institucionální podpora: RVO:67985556
    Klíčová slova: decision making * lazy learning * Bayesian learning * local model
    Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
    http://library.utia.cas.cz/separaty/2014/AS/karny-0434674.pdf

    Fully probabilistic design of decision strategies (FPD) extends Bayesian dynamic decision making. The FPD species the decision aim via so-called ideal - a probability density, which assigns high probability values to the desirable behaviours and low values to undesirable ones. The optimal decision strategy minimises the Kullback-Leibler divergence of the probability density describing the closed-loop behaviour to this ideal. In spite of the availability of explicit minimisers in the corresponding dynamic programming, it suers from the curse of dimensionality connected with complexity of the value function. Recently proposed a lazy FPD tailors lazy learning, which builds a local model around the current behaviour, to estimation of the closed-loop model with the optimal strategy. This paper adds a theoretical support to the lazy FPD and outlines its further improvement.
    Trvalý link: http://hdl.handle.net/11104/0241883

     
     
Počet záznamů: 1  

  Tyto stránky využívají soubory cookies, které usnadňují jejich prohlížení. Další informace o tom jak používáme cookies.