Abstract
Despite the rapid growth of e-commerce and the hype surrounding it, the potential of the Internet for truly transforming commerce is largely unrealized to date is because most electronic purchases are still largely nonautomated. User presence is still required in all stages of the buying process. According to the nomenclature of Maes’ group in the MIT Media Labs (Guttman, 1999; Maes, 1994), the common commerce behavior can be described with consumer buying behavior (CBB) model, which consists of six stages, namely, need identification, product brokering, merchant brokering, negotiation, purchase and delivery, and product service and evaluation.
TopIntroduction
Despite the rapid growth of e-commerce and the hype surrounding it, the potential of the Internet for truly transforming commerce is largely unrealized to date is because most electronic purchases are still largely nonautomated. User presence is still required in all stages of the buying process. According to the nomenclature of Maes’ group in the MIT Media Labs (Guttman, 1999; Maes, 1994), the common commerce behavior can be described with consumer buying behavior (CBB) model, which consists of six stages, namely, need identification, product brokering, merchant brokering, negotiation, purchase and delivery, and product service and evaluation.
The solution to automating electronic purchases could lie in the employment of software agents and relevant AI technologies in e-commerce. Software agent technologies can be used to automate several of the most time consuming stages of the buying process like product information gathering and comparison. Unlike “traditional” software, software agents are personalized (Guan, Zhu, & Maung, 2004; Wang, Guan, & Chan, 2002; Zhu, Guan, & Yang, 2000), continuously running, mobile (Guan & Yang, 1999; Guan & Yang, 2002; Ouardani, Pierre, Boucheneb, in press) and semiautonomous. These qualities are conducive for optimizing the whole buying experience and revolutionizing commerce, as we know it today. Software agents could monitor quantity and usage patterns, collect information on vendors and products that may fit the needs of the owner, evaluate different offerings, make decisions on which merchants and products to pursue, negotiate the terms of transactions with these merchants and finally place orders and make automated payments (Guan & Hua, 2003; Hua & Guan, 2000; Poh & Guan, 2000) if they are secure (Esparza, Muñoz, Soriano, Forné, 2006; Guan & Yang, 2004).
At present, there are some software agents like BargainFinder, Jango and Firefly providing ranked lists based on the prices of merchant products. However, these shopping agents fail to resolve the challenges presented next:
Seller Differentiation
Many merchants deny entry of such comparison agents into their site and refuse to be rated by these agents for this reason. Unless product comparisons can be performed in a multidimensional way, merchants will continue to show strong resistance towards admitting software agents with product comparison functions into their sites.
Buyer Differentiation
Although comparison between products based on price and features is currently available on the Internet, this feature is only useful to the buyer with relevant product knowledge. What is truly needed is a means of selecting products that match the users’ purchase requirements and preferences. These preferential purchase values include affordability, portability, brand loyalty, and other high level values that a user would usually consider in the normal purchase process.
Differentiation Change
In today’s world of rapid technological innovation, product features that are desirable yesterday may not be desirable today. Therefore, product recommendation models must be adaptable to the dynamic, changing nature of feature desirability.
The current agents also do not have complete interpretation capability of the products because vendor information is described in unstructured HTML files in a natural language. Finally, there is also the issue that the agents may need a long time in order to locate the relevant product information given the vast amounts of information available online. A more coordinated structure is required to ensure faster search time and more meaningful basis for product comparison. It is, therefore, the aim of this chapter to propose a methodology for agent learning that determines the desirability of a product and to propose an agent framework for meaningful product definition to enable value-based product evaluation and selection.
Key Terms in this Chapter
Agents: A piece of software, which acts to accomplish tasks on behalf of its user.
Client-server: A network architecture consisting of clienst or servers. Servers are computers or processes dedicated to managing files, databases or network resources. Clients are computers on which users run applications.
Heuristic: A set of rules intended to increase probability of solving problem.
Fuzzy Logic: An extension of Boolean logic that deals with the concept of partial. It is useful in clustering, expert systems and other intelligent system applications.
Genetic Algorithm: An evolutionary algorithm which generates each individual from some encoded form known as “chromosomes” or “genome”.
SAFER: An infrastructure to serve agents in e-commerce and establish the necessary mechanisms to manipulate them. The goal of SAFER is to construct standard, dynamic and evolutionary agent systems for e-commerce (Guan, 1999).
E-Commerce: The conducting of business transactions over networks and through computers.