Investigation of MAPF Problem Considering Fairness and Worst Case

Toshihiro Matsui

2025

Abstract

We investigate multiagent pathfinding problems that improve fairness and the worst case among multiple objective values for individual agents or facilities. Multiagent pathfinding (MAPF) problems have been widely studied as a fundamental class of problem in multiagent systems. A common objective to be optimized in MAPF problem settings is the total cost value of the moves and actions for all agents. Another optimization criterion is the makespan, which is equivalent to the maximum cost value for all agents in a single instance of MAPF problems. As one direction of extended MAPF problems, multiple-objective problems have been studied. In general, multiple objectives represent different types of characteristics to be simultaneously optimized for a solution that is a set of agents' paths in the case of MAPF problems, and Pareto optimality is regarded as a common criterion. Here, we focus on an optimization criterion related to fairness and the worst case among the agents themselves or the facilities affected by the agents' plans, and this is also a subset of the makespan criterion. This involves several situations where the utilities/costs, including robots' lifetimes and related humans' workloads, should be balanced among individual robots or facilities without employing external payments. The applicability of these types of criteria has been investigated in several optimization problems, including distributed constraint optimization problems, multi-objective reinforcement learning, and single-agent pathfinding problems. In this study, we address the case of MAPF problems and experimentally analyze the proposed approach to reveal its effect, as well as related issues, in this class of problems.

Paper Citation

in Harvard Style

Matsui T. (2025). Investigation of MAPF Problem Considering Fairness and Worst Case. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 224-234. DOI: 10.5220/0013389600003890

in Bibtex Style

@conference{icaart25,
author={Toshihiro Matsui},
title={Investigation of MAPF Problem Considering Fairness and Worst Case},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2025},
pages={224-234},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013389600003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Investigation of MAPF Problem Considering Fairness and Worst Case
SN - 978-989-758-737-5
AU - Matsui T.
PY - 2025
SP - 224
EP - 234
DO - 10.5220/0013389600003890
PB - SciTePress