Fairness in Cooperative Multi-Agent Systems
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@courseproject{slrfairnessperiti-mas2223,
abstract = {This systematic literature review (SLR) delves into the evolving landscape of fairness implementation and management within Multi-Agent Systems (MAS). Over the past decade, the integration of fairness considerations in MAS has gained prominence, necessitating a closer examination of methodologies and challenges associated with this integration. This review categorises several approaches, highlighting the nuanced dimensions of fairness, including distributive fairness, procedural fairness and multi-objective optimisation problem, and scrutinise strategies for managing fairness. By synthesising these findings, this review offers a comprehensive understanding of the current state of fairness in MAS, contributing to the discourse on responsible and equitable multiagent system development.},
apice = {SlrfairnessPeriti},
author = {Periti, Alex},
howpublished = {Multi-Agent Systems, A.Y. 2022/2023},
keywords = {fairness, multi-agent system, SLR},
month = feb,
sort = {survey},
supervisor = {Calegari, Roberta},
title = {Fairness in Cooperative Multi-Agent Systems},
type = {Survey Project},
url = {https://gitlab.com/pika-lab/courses/mas/projects/mas-project-periti-ay2223},
year = 2024
}
abstract = {This systematic literature review (SLR) delves into the evolving landscape of fairness implementation and management within Multi-Agent Systems (MAS). Over the past decade, the integration of fairness considerations in MAS has gained prominence, necessitating a closer examination of methodologies and challenges associated with this integration. This review categorises several approaches, highlighting the nuanced dimensions of fairness, including distributive fairness, procedural fairness and multi-objective optimisation problem, and scrutinise strategies for managing fairness. By synthesising these findings, this review offers a comprehensive understanding of the current state of fairness in MAS, contributing to the discourse on responsible and equitable multiagent system development.},
apice = {SlrfairnessPeriti},
author = {Periti, Alex},
howpublished = {Multi-Agent Systems, A.Y. 2022/2023},
keywords = {fairness, multi-agent system, SLR},
month = feb,
sort = {survey},
supervisor = {Calegari, Roberta},
title = {Fairness in Cooperative Multi-Agent Systems},
type = {Survey Project},
url = {https://gitlab.com/pika-lab/courses/mas/projects/mas-project-periti-ay2223},
year = 2024
}