Interactively Learning the User’s Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits

Mathieu Reymond; Eugenio Bargiacchi; Diederik M. Roijers; Ann Nowe

Interactively Learning the User’s Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits

Mathieu Reymond, Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowe

Onderzoeksoutput: Conference paper

Originele taal-2	English
Titel	Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024)
Redacteuren	N. Alechina , V. Dignum, M. Dastani, J.S. Sichman
Uitgeverij	International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pagina's	1611-1620
Aantal pagina's	10
Status	Published - 6 mei 2024
Evenement	23rd International Conference on Autonomous Agents and Multi-Agent Systems - AAMAS 2024 - New Zealand, Auckland, New Zealand Duur: 6 mei 2024 → 10 mei 2024 Congresnummer: 23 https://www.aamas2024-conference.auckland.ac.nz/

Conference

Conference	23rd International Conference on Autonomous Agents and Multi-Agent Systems - AAMAS 2024
Verkorte titel	AAMAS 2024
Land/Regio	New Zealand
Stad	Auckland
Periode	6/05/24 → 10/05/24
Internet adres	https://www.aamas2024-conference.auckland.ac.nz/

Toegang tot document

https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p1611.pdfLicentie: CC BY

1 Afgelopen
1 Actief

VLAAI1: Vlaams Artificiële Intelligentie Onderzoeksprogramma (VAIOP) – tweede cyclus
Nowe, A. & Vanderborght, B.
1/01/24 → 31/12/28
Project: Toegepast
FWOSB87: Grootschalige multi-agent omgevingen beheersen met model- gebaseerde reinforcement learning
Nowe, A. & Bargiacchi, E.
1/11/19 → 1/09/23
Project: Fundamenteel

Citeer dit

Reymond, M., Bargiacchi, E., Roijers, D. M., & Nowe, A. (2024). Interactively Learning the User’s Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits. In N. Alechina , V. Dignum, M. Dastani, & J. S. Sichman (editors), Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024) (blz. 1611-1620). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p1611.pdf

Reymond, Mathieu ; Bargiacchi, Eugenio ; Roijers, Diederik M. ; Nowe, Ann. / Interactively Learning the User’s Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits. Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024). redacteur / N. Alechina ; V. Dignum ; M. Dastani ; J.S. Sichman. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2024. blz. 1611-1620

@inproceedings{bd78e57306424e35897a3d73089593fb,

title = "Interactively Learning the User{\textquoteright}s Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits",

author = "Mathieu Reymond and Eugenio Bargiacchi and Roijers, {Diederik M.} and Ann Nowe",

year = "2024",

month = may,

day = "6",

language = "English",

pages = "1611--1620",

editor = "{Alechina }, {N. } and V. Dignum and M. Dastani and Sichman, { J.S.}",

booktitle = "Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024)",

publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",

note = "23rd International Conference on Autonomous Agents and Multi-Agent Systems - AAMAS 2024, AAMAS 2024 ; Conference date: 06-05-2024 Through 10-05-2024",

url = "https://www.aamas2024-conference.auckland.ac.nz/",

}

Reymond, M , Bargiacchi, E , Roijers, DM & Nowe, A 2024, Interactively Learning the User’s Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits. in N Alechina , V Dignum, M Dastani & JS Sichman (redactie), Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), blz. 1611-1620, 23rd International Conference on Autonomous Agents and Multi-Agent Systems - AAMAS 2024, Auckland, New Zealand, 6/05/24. <https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p1611.pdf>

Interactively Learning the User’s Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits. / Reymond, Mathieu ; Bargiacchi, Eugenio ; Roijers, Diederik M.; Nowe, Ann.

Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024). redactie / N. Alechina ; V. Dignum; M. Dastani; J.S. Sichman. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2024. blz. 1611-1620.

Onderzoeksoutput: Conference paper

TY - GEN

T1 - Interactively Learning the User’s Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits

AU - Reymond, Mathieu

AU - Bargiacchi, Eugenio

AU - Roijers, Diederik M.

AU - Nowe, Ann

N1 - Conference code: 23

PY - 2024/5/6

Y1 - 2024/5/6

M3 - Conference paper

SP - 1611

EP - 1620

BT - Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024)

A2 - Alechina , N.

A2 - Dignum, V.

A2 - Dastani, M.

A2 - Sichman, J.S.

PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)

T2 - 23rd International Conference on Autonomous Agents and Multi-Agent Systems - AAMAS 2024

Y2 - 6 May 2024 through 10 May 2024

ER -

Reymond M , Bargiacchi E , Roijers DM , Nowe A. Interactively Learning the User’s Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits. In Alechina N, Dignum V, Dastani M, Sichman JS, redacteurs, Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). 2024. blz. 1611-1620

Interactively Learning the User’s Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits

Conference

Toegang tot document

Projecten

VLAAI1: Vlaams Artificiële Intelligentie Onderzoeksprogramma (VAIOP) – tweede cyclus

FWOSB87: Grootschalige multi-agent omgevingen beheersen met model- gebaseerde reinforcement learning

Citeer dit