Investigation on Stochastic Local Search for Decentralized Asymmetric Multi-objective Constraint Optimization Considering Worst Case

Toshihiro Matsui

2021

Abstract

The Distributed Constraint Optimization Problem (DCOP) has been studied as a fundamental problem in multiagent cooperation. With the DCOP approach, various cooperation problems including resource allocation and collaboration among agents are represented and solved in a decentralized manner. Asymmetric Multi-Objective DCOP (AMODCOP) is an extended class of DCOPs that formalizes the situations where agents have individual objectives to be simultaneously optimized. In particular, the optimization of the worst case objective value among agents is important in practical problems. Existing works address complete solution methods including extensions with approximation. However, for large-scale and dense problems, such solution methods are insufficient. Although the existing studies also address a few simple deterministic local search methods, there are opportunities to introduce stochastic local search methods. As the basis for applying stochastic local search methods to AMODCOPs for the preferences of agents, we introduce stochastic local search methods with several optimization criteria. We experimentally analyze the influence of the optimization criteria on perturbation in the exploration process of search methods and investigate additional information propagation that extends the knowledge of the agents who are performing the local search.

Paper Citation

in Harvard Style

Matsui T. (2021). Investigation on Stochastic Local Search for Decentralized Asymmetric Multi-objective Constraint Optimization Considering Worst Case.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-484-8, pages 462-469. DOI: 10.5220/0010395504620469

in Bibtex Style

@conference{icaart21,
author={Toshihiro Matsui},
title={Investigation on Stochastic Local Search for Decentralized Asymmetric Multi-objective Constraint Optimization Considering Worst Case},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2021},
pages={462-469},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010395504620469},
isbn={978-989-758-484-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Investigation on Stochastic Local Search for Decentralized Asymmetric Multi-objective Constraint Optimization Considering Worst Case
SN - 978-989-758-484-8
AU - Matsui T.
PY - 2021
SP - 462
EP - 469
DO - 10.5220/0010395504620469