| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0353209 |
| utime |
20250206154328.4 |
| mtime |
20110104235959.9 |
| WOS |
000295049106014 |
| DOI |
10.1109/CDC.2010.5717087 |
| title
(primary) (eng) |
Preference Elicitation in Fully Probabilistic Design of Decision Strategies |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0353206 |
| ISBN |
978-1-4244-7745-6 |
| ISSN |
0743-1546 |
| title
|
Proceedings of the 49th IEEE Conference on Decision and Control |
| page_num |
5327-5332 |
| publisher |
| place |
Atlanta |
| name |
IEEE |
| year |
2010 |
|
|
| keyword |
knowledge elicitation |
| keyword |
Bayesian decision making |
| keyword |
fullz probabilistic design |
| author
(primary) |
| ARLID |
cav_un_auth*0101124 |
| name1 |
Kárný |
| name2 |
Miroslav |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101092 |
| name1 |
Guy |
| name2 |
Tatiana Valentine |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA102/08/0567 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0239566 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
Any systematic decision-making design selects a decision strategy that makes the resulting closed-loop behaviour close to the desired one. Fully Probabilistic Design (FPD) describes modelled and desired closed-loop behaviours via their distributions. The designed strategy is a minimiser of Kullback-Leibler divergence of these distributions. FPD: i) unifies modelling and aim-expressing languages; ii) directly describes multiple aims and constraints; iii) simplifies an (inevitable) approximate design as it has an explicit minimiser. The paper enriches the theory of FPD, in particular, it: i) improves its axiomatic basis; ii) quantitatively relates FPD to standard Bayesian decision making showing that the set of FPD tasks is a dense extension of Bayesian problem formulations; iii) opens a way to a systematic data-based preference elicitation, i.e., quantitative expression of decision-making aims. |
| action |
| ARLID |
cav_un_auth*0267820 |
| name |
49th IEEE Conference on Decision and Control |
| place |
Atlanta |
| dates |
14.12.2010-18.12.2010 |
| country |
US |
|
| reportyear |
2011 |
| RIV |
BB |
| permalink |
http://hdl.handle.net/11104/0006221 |
| arlyear |
2010 |
| mrcbU34 |
000295049106014 WOS |
| mrcbU63 |
cav_un_epca*0353206 Proceedings of the 49th IEEE Conference on Decision and Control 978-1-4244-7745-6 0743-1546 5327 5332 Atlanta IEEE 2010 |
|