Paper Info
Title
Learning-based automated negotiation between shipper and forwarder
Abstract
This paper studies an automated negotiation system by means of a learning-based approach. Negotiation between shipper and forwarder is used as an example in which the issues of negotiation are unit shipping price, delay penalty, due date, and shipping quantity. A data ratios method is proposed as the input of the neural network technique to explore the learning in automated negotiation with the negotiation decision functions (NDFs) developed by [Faratin, P., Sierra, C., & Jennings, N.R. (1998). Negotiation Decision Functions for Autonomous Agents. Robotics and Autonomous Systems, 24 (3), 159-182]. The concession tactic and weight of every issue offered by the opponent can be learned from this process exactly. After learning, a trade-off mechanism can be applied to achieve better negotiation result on the distance to Pareto optimal solution. Based on the results of this study, we believe that our findings can provide more insight into agent-based negotiation and can be applied to improve negotiation processes.
Year
DOI
Venue
2006
10.1016/j.cie.2006.08.008
Computers & Industrial Engineering
Keywords
Field
DocType
learning-based automated negotiation,learning,unit shipping price,autonomous agents,autonomous systems,automated negotiation,better negotiation result,negotiation decision functions,negotiation decision function,agent-based negotiation,negotiation process,automated negotiation system,trade-off mechanism,negotiation,neural network,autonomous agent
Forwarder,Autonomous agent,Pareto optimal,Artificial intelligence,Autonomous system (Internet),Adversary,Engineering,Artificial neural network,Operations management,Robotics,Negotiation
Journal
Volume
Issue
ISSN
51
3
Computers & Industrial Engineering
Citations 
PageRank 
References 
14
0.74
17
Authors
4
Name
Order
Citations
PageRank
Hsin Rau1707.20
Mou-Hsing Tsai2140.74
Chao-Wen Chen3151.10
Wei-Jung Shiang410112.14