Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

An Agent-Based Architecture for Product Selection and Evaluation under E-Commerce

An Agent-Based Architecture for Product Selection and Evaluation under E-Commerce
View Sample PDF
Author(s): Leng oon. Sim (National University of Singapore, Singapore) and Sheng-Uei Guan (National University of Singapore, Singapore)
Copyright: 2003
Pages: 13
Source title: Architectural Issues of Web-Enabled Electronic Business
Source Author(s)/Editor(s): V.K. Murthy (University of New South Wales at Australian Defence Force Academy, Australia) and Nansi Shi (University of South Australia, Australia)
DOI: 10.4018/978-1-59140-049-3.ch022


View An Agent-Based Architecture for Product Selection and Evaluation under E-Commerce on the publisher's website for pricing and purchasing information.


This chapter proposes the establishment of a trusted Trade Services entity within the electronic commerce agent framework. A Trade Services entity may be set up for each agent community. All products to be sold in the framework are to be registered with the Trade Services. The main objective of the Trade Services is to extend the current use of agents from product selection to include product evaluation in the purchase decision. To take advantage of the agent framework, the Trade Services can be a logical entity that is implemented by a community of expert agents. Each expert agent must be capable of learning about the product category it is designed to handle, as well as the ability to evaluate a specific product in the category. An approach that combines statistical analysis and fuzzy logic reasoning is proposed as one of the learning methodologies for determining the rules for product evaluation. Each feature of the registered product is statistically analyzed for any correlation with the price of the product. A regression model is then fitted to the observed data. The assumption of an intrinsically linear function for a non-linear regression model will simplify the efforts to obtain a suitable model to fit the data. The model is then used as the input membership function to indicate the desirability of the feature in the product evaluation, and the appropriate fuzzy reasoning techniques may be applied accordingly to the inputs thus obtained to arrive at a conclusion.

Related Content

Emrah Arğın. © 2022. 16 pages.
Ebru Gülbuğ Erol, Mustafa Gülsün. © 2022. 17 pages.
Yeşim Şener. © 2022. 18 pages.
Salim Kurnaz, Deimantė Žilinskienė. © 2022. 20 pages.
Dorothea Maria Bowyer, Walid El Hamad, Ciorstan Smark, Greg Evan Jones, Claire Beattie, Ying Deng. © 2022. 29 pages.
Savas S. Ates, Vildan Durmaz. © 2022. 24 pages.
Nusret Erceylan, Gaye Atilla. © 2022. 20 pages.
Body Bottom