Study on Decentralized Anytime Evolutionary Algorithm for DCOPs Containing Adversarial Agents

Toshihiro Matsui

2023

Abstract

The Distributed Constraint Optimization Problem (DCOP) is a fundamental optimization problem that represents the cooperation of multiple agents. An extended class of DCOPs contains potentially adversarial agents that can select arbitrary decisions or the worst one, and the goal is to find a safe solution under the worst case by emulating adversarial agents. Such problems are important for addressing risky situations in real world applications. Although several exact solution methods based on distributed asynchronous game-tree search for the case have been studied, their scalability is limited by the tree-width of constraint graphs that represent the DCOPs. We study the application of decentralized optimization methods based on an anytime evolutionary algorithm for DCOPs to the cases containing adversarial agents. We employ solution methods to minimize upper bound cost values, investigate several heuristic unbounded methods, and experimentally evaluate our proposed approach.

Paper Citation

in Harvard Style

Matsui T. (2023). Study on Decentralized Anytime Evolutionary Algorithm for DCOPs Containing Adversarial Agents. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-623-1, pages 338-346. DOI: 10.5220/0011758800003393

in Bibtex Style

@conference{icaart23,
author={Toshihiro Matsui},
title={Study on Decentralized Anytime Evolutionary Algorithm for DCOPs Containing Adversarial Agents},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2023},
pages={338-346},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011758800003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Study on Decentralized Anytime Evolutionary Algorithm for DCOPs Containing Adversarial Agents
SN - 978-989-758-623-1
AU - Matsui T.
PY - 2023
SP - 338
EP - 346
DO - 10.5220/0011758800003393