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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F14%3A00434674%21RIV15-GA0-67985556
rdf:type
n3:Vysledek skos:Concept
dcterms:description
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. 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.
dcterms:title
Lazy Fully Probabilistic Design of Decision Strategies Lazy Fully Probabilistic Design of Decision Strategies
skos:prefLabel
Lazy Fully Probabilistic Design of Decision Strategies Lazy Fully Probabilistic Design of Decision Strategies
skos:notation
RIV/67985556:_____/14:00434674!RIV15-GA0-67985556
n4:aktivita
n18:P n18:I
n4:aktivity
I, P(GA13-13502S)
n4:dodaniDat
n12:2015
n4:domaciTvurceVysledku
n6:6482279 n6:4780280 n6:6585256
n4:druhVysledku
n14:D
n4:duvernostUdaju
n21:S
n4:entitaPredkladatele
n10:predkladatel
n4:idSjednocenehoVysledku
25790
n4:idVysledku
RIV/67985556:_____/14:00434674
n4:jazykVysledku
n11:eng
n4:klicovaSlova
decision making; lazy learning; Bayesian learning; local model
n4:klicoveSlovo
n7:local%20model n7:decision%20making n7:lazy%20learning n7:Bayesian%20learning
n4:kontrolniKodProRIV
[5F31673EA014]
n4:mistoKonaniAkce
Hong Kong and Macao
n4:mistoVydani
Cham
n4:nazevZdroje
Advances in Neural Networks – ISNN 2014
n4:obor
n5:BB
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:projekt
n19:GA13-13502S
n4:rokUplatneniVysledku
n12:2014
n4:tvurceVysledku
Guy, Tatiana Valentine Macek, Karel Kárný, Miroslav
n4:typAkce
n20:WRD
n4:zahajeniAkce
2014-11-28+01:00
s:numberOfPages
10
n8:doi
10.1007/978-3-319-12436-0_16
n16:hasPublisher
Springer-Verlag
n17:isbn
978-3-319-12435-3