Adaptive learning to optimize resource management in a multi-agent framework

Chao Chen, Leen-Kiat Soh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In a collaborative multi-agent framework where the agents provide services to their local customers, they are encouraged to work collaboratively to improve group services as a community. The dual objectives raise the question of how agents should distribute their local resource given large number of tasks from the local customer and the community. In this paper, we propose an adaptive learning mechanism that can be embedded into an agent's decision making as an individual as well as a member of a team. It enables agents to make decisions based on the observation of the current quality of service and the distribution of the different types of tasks encountered in the past. In turn, an agent estimates the future task distribution and decides how to handle tasks initiated in the community. From the task initiator's point of view, the learning is to observe the service quality and reduce redundant initiations. From the task responder's point of view, the goal is to distribute the amount of its resources to the most needed tasks.

Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Artificial Intelligence, IC-AI'04
EditorsH.R. Arabnia
Pages386-389
Number of pages4
StatePublished - Dec 1 2004
EventProceedings of the International Conference on Artificial Intelligence, IC-AI'04 - Las Vegas, NV, United States
Duration: Jun 21 2004Jun 24 2004

Publication series

NameProceedings of the International Conference on Artificial Intelligence, IC-AI'04
Volume1

Conference

ConferenceProceedings of the International Conference on Artificial Intelligence, IC-AI'04
CountryUnited States
CityLas Vegas, NV
Period6/21/046/24/04

Fingerprint

Quality of service
Decision making

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Chen, C., & Soh, L-K. (2004). Adaptive learning to optimize resource management in a multi-agent framework. In H. R. Arabnia (Ed.), Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 (pp. 386-389). (Proceedings of the International Conference on Artificial Intelligence, IC-AI'04; Vol. 1).

Adaptive learning to optimize resource management in a multi-agent framework. / Chen, Chao; Soh, Leen-Kiat.

Proceedings of the International Conference on Artificial Intelligence, IC-AI'04. ed. / H.R. Arabnia. 2004. p. 386-389 (Proceedings of the International Conference on Artificial Intelligence, IC-AI'04; Vol. 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chen, C & Soh, L-K 2004, Adaptive learning to optimize resource management in a multi-agent framework. in HR Arabnia (ed.), Proceedings of the International Conference on Artificial Intelligence, IC-AI'04. Proceedings of the International Conference on Artificial Intelligence, IC-AI'04, vol. 1, pp. 386-389, Proceedings of the International Conference on Artificial Intelligence, IC-AI'04, Las Vegas, NV, United States, 6/21/04.
Chen C, Soh L-K. Adaptive learning to optimize resource management in a multi-agent framework. In Arabnia HR, editor, Proceedings of the International Conference on Artificial Intelligence, IC-AI'04. 2004. p. 386-389. (Proceedings of the International Conference on Artificial Intelligence, IC-AI'04).
Chen, Chao ; Soh, Leen-Kiat. / Adaptive learning to optimize resource management in a multi-agent framework. Proceedings of the International Conference on Artificial Intelligence, IC-AI'04. editor / H.R. Arabnia. 2004. pp. 386-389 (Proceedings of the International Conference on Artificial Intelligence, IC-AI'04).
@inproceedings{a558c8ccdf824cbca4e9535c2900cd9a,
title = "Adaptive learning to optimize resource management in a multi-agent framework",
abstract = "In a collaborative multi-agent framework where the agents provide services to their local customers, they are encouraged to work collaboratively to improve group services as a community. The dual objectives raise the question of how agents should distribute their local resource given large number of tasks from the local customer and the community. In this paper, we propose an adaptive learning mechanism that can be embedded into an agent's decision making as an individual as well as a member of a team. It enables agents to make decisions based on the observation of the current quality of service and the distribution of the different types of tasks encountered in the past. In turn, an agent estimates the future task distribution and decides how to handle tasks initiated in the community. From the task initiator's point of view, the learning is to observe the service quality and reduce redundant initiations. From the task responder's point of view, the goal is to distribute the amount of its resources to the most needed tasks.",
author = "Chao Chen and Leen-Kiat Soh",
year = "2004",
month = "12",
day = "1",
language = "English (US)",
isbn = "1932415335",
series = "Proceedings of the International Conference on Artificial Intelligence, IC-AI'04",
pages = "386--389",
editor = "H.R. Arabnia",
booktitle = "Proceedings of the International Conference on Artificial Intelligence, IC-AI'04",

}

TY - GEN

T1 - Adaptive learning to optimize resource management in a multi-agent framework

AU - Chen, Chao

AU - Soh, Leen-Kiat

PY - 2004/12/1

Y1 - 2004/12/1

N2 - In a collaborative multi-agent framework where the agents provide services to their local customers, they are encouraged to work collaboratively to improve group services as a community. The dual objectives raise the question of how agents should distribute their local resource given large number of tasks from the local customer and the community. In this paper, we propose an adaptive learning mechanism that can be embedded into an agent's decision making as an individual as well as a member of a team. It enables agents to make decisions based on the observation of the current quality of service and the distribution of the different types of tasks encountered in the past. In turn, an agent estimates the future task distribution and decides how to handle tasks initiated in the community. From the task initiator's point of view, the learning is to observe the service quality and reduce redundant initiations. From the task responder's point of view, the goal is to distribute the amount of its resources to the most needed tasks.

AB - In a collaborative multi-agent framework where the agents provide services to their local customers, they are encouraged to work collaboratively to improve group services as a community. The dual objectives raise the question of how agents should distribute their local resource given large number of tasks from the local customer and the community. In this paper, we propose an adaptive learning mechanism that can be embedded into an agent's decision making as an individual as well as a member of a team. It enables agents to make decisions based on the observation of the current quality of service and the distribution of the different types of tasks encountered in the past. In turn, an agent estimates the future task distribution and decides how to handle tasks initiated in the community. From the task initiator's point of view, the learning is to observe the service quality and reduce redundant initiations. From the task responder's point of view, the goal is to distribute the amount of its resources to the most needed tasks.

UR - http://www.scopus.com/inward/record.url?scp=12744281513&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=12744281513&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:12744281513

SN - 1932415335

SN - 9781932415339

T3 - Proceedings of the International Conference on Artificial Intelligence, IC-AI'04

SP - 386

EP - 389

BT - Proceedings of the International Conference on Artificial Intelligence, IC-AI'04

A2 - Arabnia, H.R.

ER -