| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0434674 |
| utime |
20240103205005.2 |
| mtime |
20150106235959.9 |
| SCOPUS |
84915749481 |
| WOS |
000354869400016 |
| DOI |
10.1007/978-3-319-12436-0_16 |
| title
(primary) (eng) |
Lazy Fully Probabilistic Design of Decision Strategies |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0434673 |
| ISBN |
978-3-319-12435-3 |
| title
|
Advances in Neural Networks – ISNN 2014 |
| part_title |
Lecture Notes in Computer Science |
| page_num |
140-149 |
| publisher |
| place |
Cham |
| name |
Springer |
| year |
2014 |
|
| editor |
|
| editor |
|
| editor |
|
|
| keyword |
decision making |
| keyword |
lazy learning |
| keyword |
Bayesian learning |
| keyword |
local model |
| author
(primary) |
| ARLID |
cav_un_auth*0101124 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| full_dept |
Department of Adaptive Systems |
| name1 |
Kárný |
| name2 |
Miroslav |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0292010 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| name1 |
Macek |
| name2 |
Karel |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101092 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| name1 |
Guy |
| name2 |
Tatiana Valentine |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0292725 |
| project_id |
GA13-13502S |
| agency |
GA ČR |
|
| abstract
(eng) |
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. |
| action |
| ARLID |
cav_un_auth*0309231 |
| name |
11th International Symposium on Neural Networks, ISNN 2014 |
| dates |
28.11.2014-01.12.2014 |
| place |
Hong Kong and Macao |
| country |
CN |
|
| RIV |
BB |
| reportyear |
2015 |
| num_of_auth |
3 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0241883 |
| confidential |
S |
| arlyear |
2014 |
| mrcbU14 |
84915749481 SCOPUS |
| mrcbU34 |
000354869400016 WOS |
| mrcbU63 |
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 |
| mrcbU67 |
Zhigang Zeng 340 |
| mrcbU67 |
Yangmin Li 340 |
| mrcbU67 |
King Irwin 340 |
|