0434674 20240103205005.220150106235959.9 84915749481 000354869400016 10.1007/978-3-319-12436-0_16 10 s. P cav_un_epca*0434673978-3-319-12435-3Lecture Notes in Computer Science140-149ChamSpringer2014ZhigangZengYangminLiKingIrwin decision making lazy learning Bayesian learning local model cav_un_auth*0101124 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Kárný Miroslav UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0292010 Adaptivní systémy Department of Adaptive Systems AS AS Macek Karel UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101092 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Guy Tatiana Valentine UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2014/AS/karny-0434674.pdf cav_un_auth*0292725 GA13-13502S GA ČR 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. cav_un_auth*0309231 11th International Symposium on Neural Networks, ISNN 2014 28.11.2014-01.12.2014 Hong Kong and Macao CN BB 2015 3 PR RVO:67985556 http://hdl.handle.net/11104/0241883 S 2014 84915749481 SCOPUS 000354869400016 WOS cav_un_epca*0434673 Advances in Neural Networks – ISNN 2014 Lecture Notes in Computer Science 978-3-319-12435-3 140 149 Advances in Neural Networks – ISNN 2014 Cham Springer 2014 Lecture Notes in Computer Science XVI 8866 Zhigang Zeng 340 Yangmin Li 340 King Irwin 340