| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0477182 |
| utime |
20240103214411.5 |
| mtime |
20170821235959.9 |
| SCOPUS |
85025121692 |
| WOS |
000432996600014 |
| DOI |
10.1007/978-3-319-61581-3_14 |
| title
(primary) (eng) |
Solving Trajectory Optimization Problems by Influence Diagrams |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0476601 |
| ISBN |
978-3-319-61580-6 |
| title
|
Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017 |
| page_num |
146-155 |
| publisher |
| place |
Cham |
| name |
Springer |
| year |
2017 |
|
| editor |
|
| editor |
|
| editor |
|
|
| keyword |
Influence diagrams |
| keyword |
Probabilistic graphical models |
| keyword |
Optimal control theory |
| keyword |
Brachistochrone problem |
| keyword |
Goddard problem |
| author
(primary) |
| ARLID |
cav_un_auth*0101228 |
| name1 |
Vomlel |
| name2 |
Jiří |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept (eng) |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department (eng) |
MTR |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0216188 |
| name1 |
Kratochvíl |
| name2 |
Václav |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| full_dept |
Department of Decision Making Theory |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0332303 |
| project_id |
GA16-12010S |
| agency |
GA ČR |
| country |
CZ |
|
| project |
| ARLID |
cav_un_auth*0348851 |
| project_id |
GA17-08182S |
| agency |
GA ČR |
|
| abstract
(eng) |
Influence diagrams are decision-theoretic extensions of Bayesian networks. In this paper we show how influence diagrams can be used to solve trajectory optimization problems. These problems are traditionally solved by methods of optimal control theory but influence diagrams offer an alternative that brings benefits over the traditional approaches. We describe how a trajectory optimization problem can be represented as an influence diagram. We illustrate our approach on two well-known trajectory optimization problems – the Brachistochrone Problem and the Goddard Problem. We present results of numerical experiments on these two problems, compare influence diagrams with optimal control methods, and discuss the benefits of influence diagrams. |
| action |
| ARLID |
cav_un_auth*0348187 |
| name |
ECSQARU: European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty |
| dates |
20170710 |
| mrcbC20-s |
20170714 |
| place |
Lugano |
| country |
CH |
|
| RIV |
JD |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20205 |
| reportyear |
2018 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0273650 |
| mrcbC62 |
1 |
| confidential |
S |
| mrcbC83 |
RIV/67985556:_____/17:00477182!RIV18-AV0-67985556 191975676 Doplnění UT WOS |
| mrcbC83 |
RIV/67985556:_____/17:00477182!RIV18-GA0-67985556 191965021 Doplnění UT WOS |
| mrcbC86 |
3+4 Proceedings Paper Computer Science Artificial Intelligence|Logic |
| mrcbC86 |
3+4 Proceedings Paper Computer Science Artificial Intelligence|Logic |
| mrcbC86 |
3+4 Proceedings Paper Computer Science Artificial Intelligence|Logic |
| arlyear |
2017 |
| mrcbU14 |
85025121692 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000432996600014 WOS |
| mrcbU63 |
cav_un_epca*0476601 Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017 Springer 2017 Cham 146 155 978-3-319-61580-6 Lecture Notes in Computer Science 10369 |
| mrcbU67 |
340 Antonucci A. |
| mrcbU67 |
340 Cholvy L. |
| mrcbU67 |
340 Papini O. |
|