Study on Applying Decentralized Evolutionary Algorithm to Asymmetric Multi-objective DCOPs with Fairness and Worst Case

Toshihiro Matsui

2022

Abstract

The Distributed Constraint Optimization Problem (DCOP) is a fundamental research area on cooperative problem solving in multiagent systems. An extended class of DCOPs represents a situation where each agent locally evaluates its partial problem with its individual constraints and objective functions on the variables shared by neighboring agents. This is a multi-objective problem on the preference of individual agents, and a set of aggregation and comparison operators is employed for a metric of social welfare among the agents. We concentrate on the case of social welfare criteria based on leximin/leximax that captures fairness among agents. Since the constraints in the practical settings of asymmetric multi-objective DCOPs are too dense for exact solution methods, scalable but inexact solution methods are necessary. We focus on employing a version of an evolutionary algorithm called AED which was designed for the original class of DCOPs. We apply the AED algorithm to asymmetric multi-objective DCOPs to handle asymmetry. We also replace the criteria in the sampling process by one of the social welfare criteria and experimentally investigate the sampling criteria in the search process.

Paper Citation

in Harvard Style

Matsui T. (2022). Study on Applying Decentralized Evolutionary Algorithm to Asymmetric Multi-objective DCOPs with Fairness and Worst Case. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-547-0, pages 417-424. DOI: 10.5220/0010919500003116

in Bibtex Style

@conference{icaart22,
author={Toshihiro Matsui},
title={Study on Applying Decentralized Evolutionary Algorithm to Asymmetric Multi-objective DCOPs with Fairness and Worst Case},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2022},
pages={417-424},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010919500003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Study on Applying Decentralized Evolutionary Algorithm to Asymmetric Multi-objective DCOPs with Fairness and Worst Case
SN - 978-989-758-547-0
AU - Matsui T.
PY - 2022
SP - 417
EP - 424
DO - 10.5220/0010919500003116