MALL: A Multi-Agent Learning Language for Competitive and Uncertain Environments

Sidi O. SOUEINA; Behrouz Homayoun FAR; Teruaki KATSUBE; Zenya KOONO

MALL: A Multi-Agent Learning Language for Competitive and Uncertain Environments

Sidi O. SOUEINA, Behrouz Homayoun FAR, Teruaki KATSUBE, Zenya KOONO

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A Multi-Agent Learning Language (MALL) is defined as being necessary for agents in environments where they encounter crucial situations in which they have to learn about the environment, other parties moves and strategies, and then construct an optimal plan. The language is based on two major factors, the level of certainty in fully monitoring (surveying) the agents and the environment, and optimal plan construction, in an autonomous way. Most of the work related to software agents is based on the assumption that other agents are trustworthy. In the growing Internet environment this may not be true. The proposed new learning language allows agents to learn about the environment and the strategies of their opponents while devising their own plans. The language is being tested in our project of software agents for Electronic Commerce that operates in various security zones. The language is flexible and adaptable to a variety of agents applications.

Publication: IEICE TRANSACTIONS on Information Vol.E81-D No.12 pp.1339-1349

Publication Date: 1998/12/25

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Type of Manuscript: Special Section PAPER (Special Issue on Knowledge-Based Software Engineering)

Category: Theory and Methodology

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Sidi O. SOUEINA, Behrouz Homayoun FAR, Teruaki KATSUBE, Zenya KOONO, "MALL: A Multi-Agent Learning Language for Competitive and Uncertain Environments" in IEICE TRANSACTIONS on Information, vol. E81-D, no. 12, pp. 1339-1349, December 1998, doi: .
Abstract: A Multi-Agent Learning Language (MALL) is defined as being necessary for agents in environments where they encounter crucial situations in which they have to learn about the environment, other parties moves and strategies, and then construct an optimal plan. The language is based on two major factors, the level of certainty in fully monitoring (surveying) the agents and the environment, and optimal plan construction, in an autonomous way. Most of the work related to software agents is based on the assumption that other agents are trustworthy. In the growing Internet environment this may not be true. The proposed new learning language allows agents to learn about the environment and the strategies of their opponents while devising their own plans. The language is being tested in our project of software agents for Electronic Commerce that operates in various security zones. The language is flexible and adaptable to a variety of agents applications.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e81-d_12_1339/_p

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@ARTICLE{e81-d_12_1339,
author={Sidi O. SOUEINA, Behrouz Homayoun FAR, Teruaki KATSUBE, Zenya KOONO, },
journal={IEICE TRANSACTIONS on Information},
title={MALL: A Multi-Agent Learning Language for Competitive and Uncertain Environments},
year={1998},
volume={E81-D},
number={12},
pages={1339-1349},
abstract={A Multi-Agent Learning Language (MALL) is defined as being necessary for agents in environments where they encounter crucial situations in which they have to learn about the environment, other parties moves and strategies, and then construct an optimal plan. The language is based on two major factors, the level of certainty in fully monitoring (surveying) the agents and the environment, and optimal plan construction, in an autonomous way. Most of the work related to software agents is based on the assumption that other agents are trustworthy. In the growing Internet environment this may not be true. The proposed new learning language allows agents to learn about the environment and the strategies of their opponents while devising their own plans. The language is being tested in our project of software agents for Electronic Commerce that operates in various security zones. The language is flexible and adaptable to a variety of agents applications.},
keywords={},
doi={},
ISSN={},
month={December},}

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TY - JOUR
TI - MALL: A Multi-Agent Learning Language for Competitive and Uncertain Environments
T2 - IEICE TRANSACTIONS on Information
SP - 1339
EP - 1349
AU - Sidi O. SOUEINA
AU - Behrouz Homayoun FAR
AU - Teruaki KATSUBE
AU - Zenya KOONO
PY - 1998
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E81-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 1998
AB - A Multi-Agent Learning Language (MALL) is defined as being necessary for agents in environments where they encounter crucial situations in which they have to learn about the environment, other parties moves and strategies, and then construct an optimal plan. The language is based on two major factors, the level of certainty in fully monitoring (surveying) the agents and the environment, and optimal plan construction, in an autonomous way. Most of the work related to software agents is based on the assumption that other agents are trustworthy. In the growing Internet environment this may not be true. The proposed new learning language allows agents to learn about the environment and the strategies of their opponents while devising their own plans. The language is being tested in our project of software agents for Electronic Commerce that operates in various security zones. The language is flexible and adaptable to a variety of agents applications.
ER -