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
Web výsledku:
http://library.utia.cas.cz/separaty/2014/AS/karny-0434674.pdf

DOI: https://doi.org/10.1007/978-3-319-12436-0_16

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