Experiments with the User’s Feedback in Preference Elicitation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00575198" target="_blank" >RIV/67985556:_____/23:00575198 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Experiments with the User’s Feedback in Preference Elicitation

Original language description

This paper deals with user’s preferences (wishes). Common users are uneducated in the decision-making (DM) theory and present their preferences incompletely. That is why we elicit them from such a user during the DM. The paper works with the DM theory called fully probabilistic design (FPD). FPD models closed DM loop, made by the user and the system, by the joint probability density (pd, real pd). A joint ideal pd quantifies the user’s preferences. It assigns high probability values to preferred closed-loop behaviors and low values to undesired behaviors. The real pd should be kept near the ideal pd. By minimizing the Kullback-Leibler divergence of the real and ideal pds, the optimal decision policy is found. The presented algorithmic quantification of preferences provides ambitious but potentially reachable DM aims. It suppresses demands on tuning preference-expressing parameters. The considered ideal pd assigns high probabilities to desired (ideal) sets of states and actions. The parameters of the ideal pd (tuned during the DM via the user’s feedback) are: ▶ relative significance of respective probabilities. ▶ a parameter balancing exploration with exploitation. Their systematic tuning solves meta-DM level task, which observes the agent’s satisfaction expressed humanly by “school-marks”. It opts free parameters to reach the best marks. A formalization and solution of this meta-task were recently done, but experience with it is limited. This paper recalls the theory and provides representative samples of extensive up to now missing simulations.n

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Article name in the collection

AIABI-2022 : Artificial Intelligence and Applications for Business and Industries 2022

ISBN

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ISSN

1613-0073

e-ISSN

1613-0073

Number of pages

13

Pages from-to

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Publisher name

CEUR-WS

Place of publication

Achen

Event location

Udine

Event date

Nov 27, 2022

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

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