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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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
Title of host publicationLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
EditorsM.W. Barley, N. Kasabov
Pages67-80
Number of pages14
Volume3371
Publication statusPublished - 2005
Externally publishedYes
Event7th Pacific Rim International Workshop on Multi-Agents, PRIMA 2004 - Auckland, New Zealand
Duration: 2004 Aug 82004 Aug 13

Other

Other7th Pacific Rim International Workshop on Multi-Agents, PRIMA 2004
CountryNew Zealand
CityAuckland
Period04/8/804/8/13

Fingerprint

Matchmaking
Relevance Feedback
Negotiating
Internet
Software
Specification
Specifications
Feedback
Multi-agent Simulation
Software agents
Software Agents
Modeling
Infrastructure
Model
Simulation
Demand
Human
Experience

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Hishiyama, R., & Ishida, T. (2005). Modeling e-procurement as co-adaptive matchmaking with mutual relevance feedback. In M. W. Barley, & N. Kasabov (Eds.), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3371, pp. 67-80)

Modeling e-procurement as co-adaptive matchmaking with mutual relevance feedback. / Hishiyama, Reiko; Ishida, Toru.

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). ed. / M.W. Barley; N. Kasabov. Vol. 3371 2005. p. 67-80.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hishiyama, R & Ishida, T 2005, Modeling e-procurement as co-adaptive matchmaking with mutual relevance feedback. in MW Barley & N Kasabov (eds), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). vol. 3371, pp. 67-80, 7th Pacific Rim International Workshop on Multi-Agents, PRIMA 2004, Auckland, New Zealand, 04/8/8.
Hishiyama R, Ishida T. Modeling e-procurement as co-adaptive matchmaking with mutual relevance feedback. In Barley MW, Kasabov N, editors, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). Vol. 3371. 2005. p. 67-80
Hishiyama, Reiko ; Ishida, Toru. / Modeling e-procurement as co-adaptive matchmaking with mutual relevance feedback. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). editor / M.W. Barley ; N. Kasabov. Vol. 3371 2005. pp. 67-80
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