Online ML Self-adaptation in Face of Traps

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10474035" target="_blank" >RIV/00216208:11320/23:10474035 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ACSOS58161.2023.00023" target="_blank" >https://doi.org/10.1109/ACSOS58161.2023.00023</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACSOS58161.2023.00023" target="_blank" >10.1109/ACSOS58161.2023.00023</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Online ML Self-adaptation in Face of Traps

Popis výsledku v původním jazyce

Online machine learning (ML) is often used in selfadaptive systems to strengthen the adaptation mechanism and improve the system utility. Despite such benefits, applying online ML for self-adaptation can be challenging, and not many papers report its limitations. Recently, we experimented with applying online ML for self-adaptation of a smart farming scenario and we had faced several unexpected difficulties - traps - that, to our knowledge, are not discussed enough in the community. In this paper, we report our experience with these traps. Specifically, we discuss several traps that relate to the specification and online training of the ML-based estimators, their impact on self-adaptation, and the approach used to evaluate the estimators. Our overview of these traps provides a list of lessons learned, which can serve as guidance for other researchers and practitioners when applying online ML for self-adaptation.

Název v anglickém jazyce

Online ML Self-adaptation in Face of Traps

Popis výsledku anglicky

Online machine learning (ML) is often used in selfadaptive systems to strengthen the adaptation mechanism and improve the system utility. Despite such benefits, applying online ML for self-adaptation can be challenging, and not many papers report its limitations. Recently, we experimented with applying online ML for self-adaptation of a smart farming scenario and we had faced several unexpected difficulties - traps - that, to our knowledge, are not discussed enough in the community. In this paper, we report our experience with these traps. Specifically, we discuss several traps that relate to the specification and online training of the ML-based estimators, their impact on self-adaptation, and the approach used to evaluate the estimators. Our overview of these traps provides a list of lessons learned, which can serve as guidance for other researchers and practitioners when applying online ML for self-adaptation.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>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 statě ve sborníku

2023 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING AND SELF-ORGANIZING SYSTEMS, ACSOS

ISBN

979-8-3503-3744-0

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

57-66

Název nakladatele

IEEE COMPUTER SOC

Místo vydání

LOS ALAMITOS

Místo konání akce

Toronto

Datum konání akce

25. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001122711700007