Towards on-line tuning of adaptive-agent’s multivariate meta-parameter
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00543581" target="_blank" >RIV/67985556:_____/21:00543581 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s13042-021-01358-w" target="_blank" >https://link.springer.com/article/10.1007/s13042-021-01358-w</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s13042-021-01358-w" target="_blank" >10.1007/s13042-021-01358-w</a>
Alternative languages
Result language
angličtina
Original language name
Towards on-line tuning of adaptive-agent’s multivariate meta-parameter
Original language description
A decision-making (DM) agent models its environment and quantifes its DM preferences. An adaptive agent models them locally nearby the realisation of the behaviour of the closed DM loop. Due to this, a simple tool set often sufces for solving complex dynamic DM tasks. The inspected Bayesian agent relies on a unifed learning and optimisation framework, which works well when tailored by making a range of case-specifc options. Many of them can be made of-line. These options concern the sets of involved variables, the knowledge and preference elicitation, structure estimation, etc. Still, some metaparameters need an on-line choice. This concerns, for instance, a weight balancing exploration with exploitation, a weight refecting agent’s willingness to cooperate, a discounting factor, etc. Such options infuence, often vitally, DM quality and their adaptive tuning is needed. Specifc ways exist, for instance, a data-dependent choice of a forgetting factor serving to tracking of parameter changes. A general methodology is, however, missing. The paper opens a pathway to it. The solution uses a hierarchical feedback exploiting a generic, DM-related, observable, mismodelling indicator. The paper presents and justifes the theoretical concept, outlines and illustrates its use.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/LTC18075" target="_blank" >LTC18075: Distributed rational decision making: cooperation aspects</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Machine Learning and Cybernetics
ISSN
1868-8071
e-ISSN
1868-808X
Volume of the periodical
12
Issue of the periodical within the volume
9
Country of publishing house
DE - GERMANY
Number of pages
15
Pages from-to
2717-2731
UT code for WoS article
000663694300001
EID of the result in the Scopus database
2-s2.0-85108291105