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Decision-Making in Evolving Environments: A Bayesian Multi-Agent Bandit Framework

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

Decision-Making in Evolving Environments: A Bayesian Multi-Agent Bandit Framework. / Alsomali, Mohammad; Soriano Marcolino, Leandro; Porter, Barry et al.
AAMAS: International Conference on Autonomous Agents and Multiagent Systems. ACM, 2025.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Alsomali, M, Soriano Marcolino, L, Porter, B & Rodrigues-Filho, R 2025, Decision-Making in Evolving Environments: A Bayesian Multi-Agent Bandit Framework. in AAMAS: International Conference on Autonomous Agents and Multiagent Systems. ACM.

APA

Alsomali, M., Soriano Marcolino, L., Porter, B., & Rodrigues-Filho, R. (2025). Decision-Making in Evolving Environments: A Bayesian Multi-Agent Bandit Framework. In AAMAS: International Conference on Autonomous Agents and Multiagent Systems ACM.

Vancouver

Alsomali M, Soriano Marcolino L, Porter B, Rodrigues-Filho R. Decision-Making in Evolving Environments: A Bayesian Multi-Agent Bandit Framework. In AAMAS: International Conference on Autonomous Agents and Multiagent Systems. ACM. 2025

Author

Alsomali, Mohammad ; Soriano Marcolino, Leandro ; Porter, Barry et al. / Decision-Making in Evolving Environments : A Bayesian Multi-Agent Bandit Framework. AAMAS: International Conference on Autonomous Agents and Multiagent Systems. ACM, 2025.

Bibtex

@inproceedings{0dbcdf3a81c84470a85dbbdf78815ce3,
title = "Decision-Making in Evolving Environments: A Bayesian Multi-Agent Bandit Framework",
abstract = "We introduce DAMAS (Dynamic Adaptation through Multi-Agent Systems), a novel framework for decision-making in non-stationary environments characterized by varying reward distributions and dynamic constraints. Our framework integrates a multi-agent system with Multi-armed Bandits (MAB) algorithms and Bayesian updates. Each agent in DAMAS specializes in a particular environmental state. The system employs Bayesian estimation to continuously update the probabilities of being in each environmental state, enabling rapid adaptation to changing conditions. Our evaluation of DAMAS included both synthetic environments and real-world web server workloads.",
author = "Mohammad Alsomali and {Soriano Marcolino}, Leandro and Barry Porter and Roberto Rodrigues-Filho",
year = "2025",
language = "English",
booktitle = "AAMAS",
publisher = "ACM",

}

RIS

TY - GEN

T1 - Decision-Making in Evolving Environments

T2 - A Bayesian Multi-Agent Bandit Framework

AU - Alsomali, Mohammad

AU - Soriano Marcolino, Leandro

AU - Porter, Barry

AU - Rodrigues-Filho, Roberto

PY - 2025

Y1 - 2025

N2 - We introduce DAMAS (Dynamic Adaptation through Multi-Agent Systems), a novel framework for decision-making in non-stationary environments characterized by varying reward distributions and dynamic constraints. Our framework integrates a multi-agent system with Multi-armed Bandits (MAB) algorithms and Bayesian updates. Each agent in DAMAS specializes in a particular environmental state. The system employs Bayesian estimation to continuously update the probabilities of being in each environmental state, enabling rapid adaptation to changing conditions. Our evaluation of DAMAS included both synthetic environments and real-world web server workloads.

AB - We introduce DAMAS (Dynamic Adaptation through Multi-Agent Systems), a novel framework for decision-making in non-stationary environments characterized by varying reward distributions and dynamic constraints. Our framework integrates a multi-agent system with Multi-armed Bandits (MAB) algorithms and Bayesian updates. Each agent in DAMAS specializes in a particular environmental state. The system employs Bayesian estimation to continuously update the probabilities of being in each environmental state, enabling rapid adaptation to changing conditions. Our evaluation of DAMAS included both synthetic environments and real-world web server workloads.

M3 - Conference contribution/Paper

BT - AAMAS

PB - ACM

ER -