Model-based preference quantification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00573588" target="_blank" >RIV/67985556:_____/23:00573588 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0005109823003461?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0005109823003461?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.automatica.2023.111185" target="_blank" >10.1016/j.automatica.2023.111185</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Model-based preference quantification

Popis výsledku v původním jazyce

Any prescriptive theory of decision-making (DM) has to cope with the common DM agents’ inability to fully specify their preferences dependent on several attributes. The paper provides the needed preference completion and quantification for fully probabilistic design (FPD) of DM strategies. FPD (covering the usual Bayesian DM) probabilistically models the agent’s environment and quantifies its preferences via an ideal probabilistic model of the closed DM loop. The probability density (pd) models (closed-loop) behaviour, a collection of involved random variables. Its ideal twin is high on desired behaviours, small on undesired and zero on forbidden ones. The FPD-optimal strategy minimises the Kullback-Leibler divergence (KLD) of the closed-loop modelling pd to the ideal twin. The exposed preference quantification chooses the optimal ideal pd from the set of pds compatible with partially-specified agent’s preferences. The optimal ideal pd minimises the KLD minima reached by the optimal strategies for respective imminent ideal pds. This preference-focused twin of the minimum KLD principle was applied to special sets of ideal pds. The paper extends them towards exploration and balancing contradictory wishes on states and actions.

Název v anglickém jazyce

Model-based preference quantification

Popis výsledku anglicky

Any prescriptive theory of decision-making (DM) has to cope with the common DM agents’ inability to fully specify their preferences dependent on several attributes. The paper provides the needed preference completion and quantification for fully probabilistic design (FPD) of DM strategies. FPD (covering the usual Bayesian DM) probabilistically models the agent’s environment and quantifies its preferences via an ideal probabilistic model of the closed DM loop. The probability density (pd) models (closed-loop) behaviour, a collection of involved random variables. Its ideal twin is high on desired behaviours, small on undesired and zero on forbidden ones. The FPD-optimal strategy minimises the Kullback-Leibler divergence (KLD) of the closed-loop modelling pd to the ideal twin. The exposed preference quantification chooses the optimal ideal pd from the set of pds compatible with partially-specified agent’s preferences. The optimal ideal pd minimises the KLD minima reached by the optimal strategies for respective imminent ideal pds. This preference-focused twin of the minimum KLD principle was applied to special sets of ideal pds. The paper extends them towards exploration and balancing contradictory wishes on states and actions.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Automatica

ISSN

0005-1098

e-ISSN

1873-2836

Svazek periodika

156

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

111185

Kód UT WoS článku

001039370200001

EID výsledku v databázi Scopus

2-s2.0-85166665633