Combining Individual and Cooperative Learning for Multiagent Negotiations

Leen Kiat Soh; Juan Luo

Combining Individual and Cooperative Learning for Multiagent Negotiations

Leen Kiat Soh, Juan Luo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we propose a distributed multi-strategy learning methodology based on case-based reasoning in which an agent conducts both individual learning by observing its environment and cooperative learning by interacting with its neighbors. Cooperative learning is generally more expensive than individual learning due to the communication and processing overhead. Thus, our methodology employs a cautious utility-based adaptive mechanism to combine the two, an interaction protocol for soliciting and exchanging information, and the idea of a chronological casebase. Here we report on experimental results on the roles and effects of the methodology in a multiagent environment.

Original language	English (US)
Title of host publication	Proceedings of the Interantional Conference on Autonomous Agents
Editors	J.S. Rosenschein, T. Sandholm, M. Wooldridge, M. Yakoo
Pages	1122-1123
Number of pages	2
Volume	2
State	Published - 2003
Event	Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 03 - Melbourne, Vic. Duration: Jul 14 2003 → Jul 18 2003

Other

Other	Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 03
City	Melbourne, Vic.
Period	7/14/03 → 7/18/03

Keywords

Case-based reasoning
Cooperative learning
Distributed learning
Multiagent systems

ASJC Scopus subject areas

General Engineering

Cite this

Soh, LK & Luo, J 2003, Combining Individual and Cooperative Learning for Multiagent Negotiations. in JS Rosenschein, T Sandholm, M Wooldridge & M Yakoo (eds), Proceedings of the Interantional Conference on Autonomous Agents. vol. 2, pp. 1122-1123, Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 03, Melbourne, Vic., 7/14/03.

@inproceedings{f9f73891ddca414590afe43fce1d4e2a,

title = "Combining Individual and Cooperative Learning for Multiagent Negotiations",

abstract = "In this paper, we propose a distributed multi-strategy learning methodology based on case-based reasoning in which an agent conducts both individual learning by observing its environment and cooperative learning by interacting with its neighbors. Cooperative learning is generally more expensive than individual learning due to the communication and processing overhead. Thus, our methodology employs a cautious utility-based adaptive mechanism to combine the two, an interaction protocol for soliciting and exchanging information, and the idea of a chronological casebase. Here we report on experimental results on the roles and effects of the methodology in a multiagent environment.",

keywords = "Case-based reasoning, Cooperative learning, Distributed learning, Multiagent systems",

author = "Soh, {Leen Kiat} and Juan Luo",

year = "2003",

language = "English (US)",

volume = "2",

pages = "1122--1123",

editor = "J.S. Rosenschein and T. Sandholm and M. Wooldridge and M. Yakoo",

booktitle = "Proceedings of the Interantional Conference on Autonomous Agents",

note = "Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 03 ; Conference date: 14-07-2003 Through 18-07-2003",

}

TY - GEN

T1 - Combining Individual and Cooperative Learning for Multiagent Negotiations

AU - Soh, Leen Kiat

AU - Luo, Juan

PY - 2003

Y1 - 2003

N2 - In this paper, we propose a distributed multi-strategy learning methodology based on case-based reasoning in which an agent conducts both individual learning by observing its environment and cooperative learning by interacting with its neighbors. Cooperative learning is generally more expensive than individual learning due to the communication and processing overhead. Thus, our methodology employs a cautious utility-based adaptive mechanism to combine the two, an interaction protocol for soliciting and exchanging information, and the idea of a chronological casebase. Here we report on experimental results on the roles and effects of the methodology in a multiagent environment.

AB - In this paper, we propose a distributed multi-strategy learning methodology based on case-based reasoning in which an agent conducts both individual learning by observing its environment and cooperative learning by interacting with its neighbors. Cooperative learning is generally more expensive than individual learning due to the communication and processing overhead. Thus, our methodology employs a cautious utility-based adaptive mechanism to combine the two, an interaction protocol for soliciting and exchanging information, and the idea of a chronological casebase. Here we report on experimental results on the roles and effects of the methodology in a multiagent environment.

KW - Case-based reasoning

KW - Cooperative learning

KW - Distributed learning

KW - Multiagent systems

UR - http://www.scopus.com/inward/record.url?scp=1142280393&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=1142280393&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:1142280393

VL - 2

SP - 1122

EP - 1123

BT - Proceedings of the Interantional Conference on Autonomous Agents

A2 - Rosenschein, J.S.

A2 - Sandholm, T.

A2 - Wooldridge, M.

A2 - Yakoo, M.

T2 - Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 03

Y2 - 14 July 2003 through 18 July 2003

ER -

Combining Individual and Cooperative Learning for Multiagent Negotiations

Abstract

Other

Keywords

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this