Fully probabilistic design of strategies with estimator

0556428 - ÚTIA 2023 RIV NL eng J - Journal Article
Kárný, Miroslav
Fully probabilistic design of strategies with estimator.
Automatica. Roč. 141, č. 1 (2022), č. článku 110269. ISSN 0005-1098. E-ISSN 1873-2836
R&D Projects: GA MŠMT(CZ) LTC18075
Institutional support: RVO:67985556
Keywords : Bayes methods * closed loop systems * decision making * dynamic programming * estimation
OECD category: Robotics and automatic control
Impact factor: 6.4, year: 2022
Method of publishing: Limited access
http://library.utia.cas.cz/separaty/2022/AS/karny-0556428.pdf https://www.sciencedirect.com/science/article/pii/S0005109822001145?via%3Dihub

The axiomatic fully probabilistic design (FDP) of decision strategies strictly extends Bayesian decision making (DM) theory. FPD also models the closed decision loop by a joint probability density (pd) of all inspected random variables, referred as behaviour. FPD expresses DM aims via an ideal pd of behaviours, unlike the usual DM. Its optimal strategy minimises Kullback–Leibler divergence (KLD) of the joint, strategy-dependent, pd of behaviours to its ideal twin. A range of FPD results confirmed its theoretical and practical strength. Curiously, no guide exists how to select a specific ideal pd for an estimator design. The paper offers it. It advocates the use of the closed-loop state notion and generalises dynamic programming so that FPD is its special case. Primarily, it provides an explorative optimised feedback that ‘‘naturally’’ diminishes exploration (gained in learning) as the learning progresses.
Permanent Link: http://hdl.handle.net/11104/0330841