DocumentCode :
169195
Title :
Multi-phase negotiation for single-item bidding
Author :
Benicio, Alberto A. ; Possebom, Ayslan T. ; Avila, Braulio C. ; Enembreck, Fabricio ; Scalabrin, Edson Emilio
Author_Institution :
Grad. Program in Comput. Sci., Pontifical Catholic Univ. of Parana (PUCPR), Curitiba, Brazil
fYear :
2014
fDate :
21-23 May 2014
Firstpage :
354
Lastpage :
359
Abstract :
This article presents a multi-phase bidding model that is primarily for reverse bidding (1:N) and can also be used later for bilateral bidding (1:1). This multi-phase approach excels by competing for the lowest price and relativizes a subtle “lose-win” relationship of its own for the reverse auction through a second phase of negotiation. The latter is limited to a bilateral relationship and is applied, if necessary, between the purchaser and the second or third best offer of the reverse auction. Experiments were conducted with stationary or adaptive negotiation agents using learning techniques to conduct negotiation policy, using a genetic algorithm to characterize the opponent´s preferences and configure the generation of interesting offers. The results showed the influence that an aggressive bidder has on the process as a whole and also what can be done to minimize this effect.
Keywords :
electronic commerce; genetic algorithms; learning (artificial intelligence); software agents; tendering; adaptive negotiation agent; aggressive bidder; bilateral bidding; bilateral relationship; genetic algorithm; learning techniques; lose-win relationship; multiphase bidding model; multiphase negotiation; opponent preferences; reverse auction; reverse bidding; single item bidding; stationary negotiation agent; Biological cells; Calcium; Equations; Genetic algorithms; Mathematical model; Proposals; Proteins; Reverse auction; bilateral negotiation; computational learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Supported Cooperative Work in Design (CSCWD), Proceedings of the 2014 IEEE 18th International Conference on
Conference_Location :
Hsinchu
Type :
conf
DOI :
10.1109/CSCWD.2014.6846869
Filename :
6846869
Link To Document :
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