Agile decision agent for service-oriented e-commerce systems

IEEE Transactions on Knowledge and Data Engineering, 2004

There is growing interest in using intelligent software agents for a variety of tasks, including navigating and retrieving information from the Internet and from databases, online shopping activities, user authentication, negotiation for resources, and decision making. This paper proposes an integrated framework for information retrieval and information filtering in the context of Internet shopping. The work focuses on applying agent technology, together with Web mining technology, to automate a series of product search and selection activities. It is based on a multiagent development platform, namely, iJADE (Intelligent Java Agent Development Environment), which supports various e-commerce applications. The framework comprises an automatic facial authentication utility and six other modules, namely, customer requirements definition, a requirement-fuzzification scheme, a fuzzy agents-negotiation scheme, a fuzzy product-selection scheme, a product-defuzzification scheme, and a product-evaluation scheme. A series of experiments were carried out and favorable results were produced in executing the framework. From an experimental point of view, we used a database of 1,020 facial images that were obtained under various conditions of facial expression, viewing perspective and size. An overall correct recognition rate of over 85 percent was attained. For the product selection test of our fuzzy shopper system, an average matching rate of more than 81 percent was achieved.

1999

Much of the attention in artificial intelligence (AI) for e-Business has focused on business to consumer transactions. Shopping bots, systems to recommend movies and books based on similar opinions by other users and news filtering agents, are just some examples. However, we feel that AI can have a larger impact on the supply chain that delivers goods and services to the end consumer. Reductions in costs and the pervasiveness of the Internet have encouraged companies to move towards using ecommerce for transactions with their business partners. Companies are willing to invest resource because of the reduced product cycle times and the lower transaction costs that they expect. A result of this movement is that companies can afford to interact with a larger number of trading partners and form project and customer specific partnerships that would have been too costly in the past. To manage a larger and more dynamic set of partnerships and to be able to take advantage of transient oppor...

There are well-documented approaches and methodologies for information system development (ISD). These approaches and methodologies can all be related to software development life cycle (SDLC), a paradigm focusing on ISD in organization and other closed-domain systems. Recent developments in agentoriented software engineering, like Gaia, extend this paradigm to cover intelligent agents and multiagent systems. However, evidence from Web-based i-DMSS like comparison-shopping agents shows distinctive patterns of development. Since there are few studies that cover the ISD paradigm for agents in an open-domain system, through three mini case studies in this chapter, we provide relatively comprehensive cases for future theory formulation.

Agent-Mediated Electronic Commerce III, 2001

2007

ABSTRACT This paper describes the powerful use of agent technology with machine learning in e-commerce. Speed and selfmanagement have become very important aspects in E-commerce, driving researchers to look around to find some other technologies that can solve the problems of E-commerce. This paper illustrates the features of agent technology and machine learning, and defines how they can support e-commerce.

2003

There are many e-commerce applications on the Web. A common shortcoming is the lack of customer service and marketing analysis tools in most e-commerce web sites. In order to overcome this problem, we have constructed an intelligent agent based on Case-Based Reasoning (CBR) and collaborative filtering, which we have included in our product recommendation system, called PCFinder. This system was four main characteristics. The first is applying novel methodologies based on CBR to an e-commerce application. We propose a heuristic to represent an Order-Based Similarity Measure, together with the method of weight modification and adaptation. The second is applying CBR and collaborative filtering techniques to make our intelligent agent more efficient and effective. We also apply clustering analysis techniques to assist our intelligent agent for grouping the customers according to their long-term profiles in order to analyze the user profiles (external attributes) and provide some suggestions of the items (internal attributes) of the product. The third is introducing a method for constructing product recommendation systems: from architecture to methodologies and from applied technologies to implementations. The last is providing a graphic-building wizard based on clustering analysis of the past purchasing history to the management staff for analyzing the marketing tendencies.

Procedia Computer Science, 2015

In this paper we present our experience in developing a fuzzy-logic based multi-agent e-commerce system capable of achieving a mutually beneficial deal for the seller and buyer using a negotiation process. We use fuzzy logic to assist users to express their preferences about a product in fuzzy terms such as low, medium and high. Our system evaluates offers based on a fuzzy utility function and feeds utility scores to a fuzzy inference system which then computes its next counter offer. Our paper presents issues involved in the development of a multi-agent system for e-commerce settings using the JADE platform-a modern agent development environment. In this paper our focus is on implementing agents of different types/roles engaged in activities usually encountered with buying and selling in an e-commerce environment. Our concluding remarks and future research are presented.

2009

International Journal of Approximate Reasoning, 2010

In this paper, we have introduced an agent based e-commerce system which recommends products to buyers as per their preferences. Initially, the agent collects the buyers' preferences in fuzzy or linguistically defined terms and based on this, presents them an ordered set of products. After obtaining the buyers' feedbacks when they actually come across the products, the seller's agent interacts with the buyer (buyer's agent), revises the products preferential order and recommends either the same set of products or a new set of similar products with the revised preferential order. The buyer's revised preferences are taken here as his/her feedback after he/she comes across with the actual products (presented products). Concepts of fuzzy logic and Fuzzy Linear Programming are used here to identify the buyer's feedbacks on the initial presentation of the products. Our methodology also measures the degree of customers' focus on the products which are finally recommended by the e-commerce agent. The product ranking obtained through buyers' initial preferences is considered here as his/her subjective information and the available information from the agents' presented products are taken as the objective information.

Expert Systems with Applications, 2002

The prosperity of electronic commerce has changed the traditional trading behaviors and more and more people are willing to conduct Internet shopping. However, the exponentially increasing information provided by the Internet enterprises causes the problem of overloaded information, and this inevitably reduces the customer's satisfaction and loyalty. One way to overcome such a problem is to build personalized recommender systems to retrieve product information that really interests the customers. For products that people may purchase relatively often, such as books and CDs, recommender systems can be built to reason about a customer's personal preferences from his purchasing history and then provide the most appropriate information services to meet his needs. On the other hand, for those commodities a general customer does not buy frequently, for example computers and home theater systems, more appropriate are the kinds of recommender systems able to retrieve optimal products based on the customer's current preferences obtained from the iterative system±customer interactions. This paper presents the above two kinds of recommender systems we have developed for supporting Internet commerce. Experimental results show the promise of our systems.