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.
|ジャーナル||Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)|
|出版ステータス||Published - 2005|
|イベント||7th Pacific Rim International Workshop on Multi-Agents, PRIMA 2004 - Auckland, New Zealand|
継続期間: 2004 8月 8 → 2004 8月 13
ASJC Scopus subject areas
- コンピュータ サイエンス（全般）