| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0483831 |
| utime |
20240103215240.2 |
| mtime |
20180102235959.9 |
| SCOPUS |
85055486182 |
| DOI |
10.1007/978-3-030-01713-2_20 |
| title
(primary) (eng) |
Lazy Fully Probabilistic Design: Application Potential |
| specification |
| page_count |
11 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0483465 |
| ISBN |
978-3-030-01712-5 |
| title
|
Multi-Agent Systems and Agreement Technologies |
| page_num |
281-291 |
| publisher |
| place |
Cham |
| name |
Springer |
| year |
2018 |
|
| editor |
| name1 |
Belardinelli |
| name2 |
F. |
|
|
| keyword |
lazy learning |
| keyword |
fully probabilistic design |
| keyword |
decision making |
| keyword |
linear quadratic Gaussian control |
| author
(primary) |
| ARLID |
cav_un_auth*0101092 |
| name1 |
Guy |
| name2 |
Tatiana Valentine |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0355639 |
| full_dept |
Department of Adaptive Systems |
| name1 |
Fakhimi Derakhshan |
| name2 |
Siavash |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0355640 |
| name1 |
Štěch |
| name2 |
Jakub |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0331019 |
| project_id |
GA16-09848S |
| agency |
GA ČR |
|
| abstract
(eng) |
The article addresses a lazy learning approach to fully probabilistic decision making when a decision maker (human or arti_cial) uses incomplete knowledge of environment and faces high computational limitations. The resulting lazy Fully Probabilistic Design (FPD) selects a decision strategy that moves a probabilistic description of the closed decision loop to a pre-speci_ed ideal description. The lazy FPD uses currently observed data to _nd past closed-loop similar to the actual ideal model. The optimal decision rule of the closest model is then used in the current step. The e_ectiveness and capability of the proposed approach are manifested through example. |
| action |
| ARLID |
cav_un_auth*0355398 |
| name |
European Conference on Multi-Agent Systems (EUMAS) 2017 /15./ |
| dates |
20171214 |
| mrcbC20-s |
20171215 |
| place |
Évry |
| country |
FR |
|
| RIV |
BC |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2019 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0278988 |
| confidential |
S |
| arlyear |
2018 |
| mrcbU14 |
85055486182 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0483465 Multi-Agent Systems and Agreement Technologies Springer 2018 Cham 281 291 978-3-030-01712-5 Lecture Notes in Artificial Intelligence 10767 |
| mrcbU67 |
340 Belardinelli F. |
|