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Selection of Adaptive Strategies on Main Agent’s Attitude Based on Historical Learning

Authors:
Guorui Jiang
Hong Guo

Keywords: adaptive strategy; historical learning; negotiation attitude; multi-agent system

Abstract:
To solve the problems of uncertainty and variability in the current automated commerce negotiation, the intelligence of agent is applied to the process of the commerce negotiation. We propose the adaptive negotiation strategies based on multi-agent negotiation by historical learning algorithm. During negotiation, the main agent, for example buyer agent, obtains historical information of the opponent, as seller, from the third party agent who stores the information of agents participated in and trade information, and then calculates the negotiation attitude values of the opponents by historical learning algorithm. Considering the information of the dynamic market environment, the main agent presents an appropriate strategy by employing the adaptive concession strategy function and the effectiveness evaluation mechanism. The research achievement of this paper is a foundation for developing a real-life Multi-agent-based commerce negotiation system in the future.

Pages: 238 to 242

Copyright: Copyright (c) IARIA, 2012

Publication date: June 24, 2012

Published in: conference

ISSN: 2308-4529

ISBN: 978-1-61208-202-8

Location: Venice, Italy

Dates: from June 24, 2012 to June 29, 2012