0477182 20240103214411.520170821235959.9 85025121692 000432996600014 10.1007/978-3-319-61581-3_14 Solving Trajectory Optimization Problems by Influence Diagrams 10 s. P cav_un_epca*0476601978-3-319-61580-6Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017146-155ChamSpringer2017AntonucciA.CholvyL.PapiniO. Influence diagrams Probabilistic graphical models Optimal control theory Brachistochrone problem Goddard problem cav_un_auth*0101228 Vomlel Jiří UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0216188 Kratochvíl Václav UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2017/MTR/vomlel-0477182.pdf cav_un_auth*0332303 GA16-12010S GA ČR CZ cav_un_auth*0348851 GA17-08182S GA ČR 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. cav_un_auth*0348187 ECSQARU: European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty 20170710 20170714 Lugano CH JD 20000 20200 20205 2018 2 PR RVO:67985556 http://hdl.handle.net/11104/0273650 1 S RIV/67985556:_____/17:00477182!RIV18-AV0-67985556 191975676 Doplnění UT WOS RIV/67985556:_____/17:00477182!RIV18-GA0-67985556 191965021 Doplnění UT WOS 3+4 Proceedings Paper Computer Science Artificial Intelligence|Logic 3+4 Proceedings Paper Computer Science Artificial Intelligence|Logic 3+4 Proceedings Paper Computer Science Artificial Intelligence|Logic 2017 85025121692 SCOPUS PUBMED 000432996600014 WOS 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 340 Antonucci A. 340 Cholvy L. 340 Papini O.