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Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey : JAAMAS Track. / Radulescu, Roxana; Mannion, Patrick; Roijers, Diederik; Nowe, Ann.

Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2020 . IFAAMAS, 2020.

Research output: Chapter in Book/Report/Conference proceedingConference paper

Harvard

Radulescu, R, Mannion, P, Roijers, D & Nowe, A 2020, Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey: JAAMAS Track. in Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2020 . IFAAMAS, The 19th International Conference on Autonomous Agents and Multi-Agent Systems 2020, Auckland, New Zealand, 9/05/20.

APA

Radulescu, R., Mannion, P., Roijers, D., & Nowe, A. (Accepted/In press). Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey: JAAMAS Track. In Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2020 IFAAMAS.

Vancouver

Radulescu R, Mannion P, Roijers D, Nowe A. Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey: JAAMAS Track. In Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2020 . IFAAMAS. 2020

Author

Radulescu, Roxana ; Mannion, Patrick ; Roijers, Diederik ; Nowe, Ann. / Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey : JAAMAS Track. Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2020 . IFAAMAS, 2020.

BibTeX

@inproceedings{05bbf81890cd4c8fb53168a81624ca89,
title = "Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey: JAAMAS Track",
abstract = "Many real-world decision problems are inherently multi-objective in nature and concern multiple actors, making multi-objective multi- agent systems a key domain to study. We argue that trade-offs between conflicting objective functions should be analysed on the basis of the utility that these trade-offs have for the users of a system. We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures and utility functions. We analyse which solution concepts apply to the different settings in our taxonomy, which allows us to offer a structured view of the field and identify promising directions for future research.",
author = "Roxana Radulescu and Patrick Mannion and Diederik Roijers and Ann Nowe",
year = "2020",
month = "3",
day = "1",
language = "English",
booktitle = "Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2020",
publisher = "IFAAMAS",

}

RIS

TY - GEN

T1 - Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey

T2 - JAAMAS Track

AU - Radulescu, Roxana

AU - Mannion, Patrick

AU - Roijers, Diederik

AU - Nowe, Ann

PY - 2020/3/1

Y1 - 2020/3/1

N2 - Many real-world decision problems are inherently multi-objective in nature and concern multiple actors, making multi-objective multi- agent systems a key domain to study. We argue that trade-offs between conflicting objective functions should be analysed on the basis of the utility that these trade-offs have for the users of a system. We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures and utility functions. We analyse which solution concepts apply to the different settings in our taxonomy, which allows us to offer a structured view of the field and identify promising directions for future research.

AB - Many real-world decision problems are inherently multi-objective in nature and concern multiple actors, making multi-objective multi- agent systems a key domain to study. We argue that trade-offs between conflicting objective functions should be analysed on the basis of the utility that these trade-offs have for the users of a system. We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures and utility functions. We analyse which solution concepts apply to the different settings in our taxonomy, which allows us to offer a structured view of the field and identify promising directions for future research.

M3 - Conference paper

BT - Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2020

PB - IFAAMAS

ER -

ID: 49911076