A hybrid negotiation strategy mechanism in an automated negotiation system

Authors:
Sheng Zhang;Song Ye;Fillia Makedon;James Ford
Affiliations:
Dartmouth College, Hanover, NH;Dartmouth College, Hanover, NH;Dartmouth College, Hanover, NH;Dartmouth College, Hanover, NH
Venue:
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Year:
2004

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Abstract

This paper describes a hybrid negotiation strategy mechanism using a strategy pool framework that allows negotiation agents more flexibility and robustness in an automated negotiation system. The strategy pool framework supports: a) dynamically assigning an appropriate negotiation strategy to a negotiation agent according to the current negotiation environment and b) creating new negotiation rules by learning from past negotiations. Learning forms we use here for the framework are Feed Forward Back Propagation (FFBP) neural networks and multidimensional inter-transaction association rules mining.