Discounted fully probabilistic design of decision rules

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00599786" target="_blank" >RIV/67985556:_____/24:00599786 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0020025524014920?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0020025524014920?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ins.2024.121578" target="_blank" >10.1016/j.ins.2024.121578</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Discounted fully probabilistic design of decision rules

Popis výsledku v původním jazyce

Axiomatic fully probabilistic design (FPD) of optimal decision rules strictly extends the decision making (DM) theory represented by Markov decision processes (MDP). This means that any MDP task can be approximated by an explicitly found FPD task whereas many FPD tasks have no MDP equivalent. MDP and FPD model the closed loop — the coupling of an agent and its environment — via a joint probability density (pd) relating the involved random variables, referred to as behaviour. Unlike MDP, FPD quantifies agent’s aims and constraints by an ideal pd. The ideal pd is high on the desired behaviours, small on undesired behaviours and zero on forbidden ones. FPD selects the optimal decision rules as the minimiser of Kullback-Leibler’s divergence of the closed-loop-modelling pd to its ideal twin. The proximity measure choice follows from the FPD axiomatics. MDP minimises the expected total loss, which is usually the sum of discounted partial losses. The discounting reflects the decreasing importance of future losses. It also diminishes the influence of errors caused by:n▶ the imperfection of the employed environment model.n▶ roughly-expressed aims.n▶ the approximate learning and decision-rules design.nThe established FPD cannot currently account for these important features. The paper elaborates the missing discounted version of FPD. This non-trivial filling of the gap in FPD also employs an extension of dynamic programming, which is of an independent interest.

Název v anglickém jazyce

Discounted fully probabilistic design of decision rules

Popis výsledku anglicky

Axiomatic fully probabilistic design (FPD) of optimal decision rules strictly extends the decision making (DM) theory represented by Markov decision processes (MDP). This means that any MDP task can be approximated by an explicitly found FPD task whereas many FPD tasks have no MDP equivalent. MDP and FPD model the closed loop — the coupling of an agent and its environment — via a joint probability density (pd) relating the involved random variables, referred to as behaviour. Unlike MDP, FPD quantifies agent’s aims and constraints by an ideal pd. The ideal pd is high on the desired behaviours, small on undesired behaviours and zero on forbidden ones. FPD selects the optimal decision rules as the minimiser of Kullback-Leibler’s divergence of the closed-loop-modelling pd to its ideal twin. The proximity measure choice follows from the FPD axiomatics. MDP minimises the expected total loss, which is usually the sum of discounted partial losses. The discounting reflects the decreasing importance of future losses. It also diminishes the influence of errors caused by:n▶ the imperfection of the employed environment model.n▶ roughly-expressed aims.n▶ the approximate learning and decision-rules design.nThe established FPD cannot currently account for these important features. The paper elaborates the missing discounted version of FPD. This non-trivial filling of the gap in FPD also employs an extension of dynamic programming, which is of an independent interest.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Information Sciences

ISSN

0020-0255

e-ISSN

1872-6291

Svazek periodika

690

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

121578

Kód UT WoS článku

001369164400001

EID výsledku v databázi Scopus

2-s2.0-85207087101