Modeling e-procurement as co-adaptive matchmaking with mutual relevance feedback

Reiko Hishiyama*, Toru Ishida

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)


This paper proposes a new e-procurement model for a large number of buyers and sellers interacting via the Internet. The goal of e-procurement is to create a satisfactory match between buyers' demand and sellers' supply. From our real-world experience, we view e-procurement as a process of negotiation to increase the matching quality of two corresponding specifications: one for buyers' demand and another for sellers' supply. To model scalable e-procurement, we propose a co-adaptive matchmaking mechanism using mutual relevance feedback. In order to understand the nature of the mechanism, we have developed two types of software agents, called e-buyers and e-sellers, to simulate human buyers and sellers. Multiagent simulation results show that the matching quality is incrementally improved if agents adaptively change their specifications. A realistic example is also provided to discuss how to extend our simulation to real-world e-procurement infrastructure.

Original languageEnglish
Pages (from-to)67-80
Number of pages14
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Publication statusPublished - 2005
Externally publishedYes
Event7th Pacific Rim International Workshop on Multi-Agents, PRIMA 2004 - Auckland, New Zealand
Duration: 2004 Aug 82004 Aug 13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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