EC-XAMAS: SUPPORTING E-COMMERCE ACTIVITIES BY AN XML-BASED ADAPTIVE MULTI-AGENT SYSTEM

In this aricle we propose EC-XAMAS, an XML-based adaptive multi-agent system for handling e-commerce activities. More specifically, EC-XAMAS aims at supporting a customer visiting an e-commerce site in the search of products and/or services present therein and appearing to be appealing according to her past interests and behavior. The system is adaptive with respect to the profile of both the customer and the device she is exploiting for visiting the site. Finally, it is XML based since XML is exploited for both storing the agent ontologies and handling the agent communication.     

一种切实可行的酒店电子商务解决方案

根据目前酒店上网的现状分析,提出了Web hotel中间商的电子商务解决方案,由网络中间商负责管理和运行Web hotel服务器,并设计Web hotel客户端,为酒店的网上营销和订房管理提供有力的保障工具。这是一条适合我国国情的酒店预订业务电子商务解决方案,可以为众多的中小酒店开拓网络销售渠道,并为中小型酒店开展电子商务提供可行的技术支持。     

Specification and testing of autonomous agents in e‐commerce systems

This paper presents a generic formal framework to specify and test autonomous e‐commerce agents. First, the formalism to represent the behaviour of agents is introduced. The corresponding machinery to define how implementations can be tested follows. Two testing approaches are considered. The first of them, which can be called active, is based on stimulating the implementation under test (IUT) with a test. The peculiarity is that tests will be defined as a special case of autonomous e‐commerce agent. The second approach, which can be called passive, consists of observing the behaviour of the tested agent in an environment containing other agents. As a case study the framework is applied to the e‐commerce system Kasbah. Copyright © 2005 John Wiley & Sons, Ltd.     

Agents in object‐oriented software engineering

Software engineers of multi‐agent systems (MASs) are faced with different concerns such as autonomy, adaptation, interaction, collaboration, learning, and mobility, which are essentially different from classical concerns addressed in object‐oriented software engineering. MAS developers, however, have relied mostly on object‐oriented design techniques and programming languages, such as Java. This often leads to a poor separation of MAS concerns and in turn to the production of MASs that are difficult to maintain and reuse. This paper discusses software engineering approaches for MASs, and presents a new method for integrating agents into object‐oriented software engineering from an early stage of design. The proposed approach encourages the separate handling of MAS concerns, and provides a disciplined scheme for their composition. Our proposal explores the benefits of aspect‐oriented software development for the incorporation of agents into object‐oriented systems. We also illustrate our aspect‐oriented approach through the Portalware multi‐agent system, a Web‐based environment for the development of e‐commerce portals. Copyright © 2004 John Wiley & Sons, Ltd.     

The effect of e‐commerce on the integration of IT structure and brand architecture

Abstract. A company's information technology (IT) structure and its brand architecture are intended to minimize transaction costs both within the organization and between the organization and its customers. Business‐to‐Consumer (B2C) e‐commerce fundamentally alters the structure of those transaction costs relevant to the IT structure and the brand architecture. We conducted a survey among 102 chief information officers and chief marketing officers in 67 of the 100 most important B2C enterprises in Austria. The results show that companies typically implement a certain set of changes in the IT structure and the brand architecture if B2C e‐commerce is highly important to them and that these changes result in a stronger integration within and between the IT structure and the brand architecture. B2C e‐commerce projects thus require closely aligned conceptual, organizational and financial measures in both areas.     

Eliciting Customer Preferences for Products From Navigation Behavior on the Web: A Multicriteria Decision Approach With Implicit Feedback

The goal of raising customer loyalty in electronic commerce requires an emphasis on one-to-one marketing and personalized services. To this end, it is essential to understand individual customer preferences for products. In this paper, we present a method for identifying customer preferences and recommending the most appropriate product. The identification and recommendation of such products are all based on the use of customer's real-time web usage behavior, including activities such as viewing, basket placement, and purchasing of products. Therefore, in this approach, we do not force a customer to explicitly express his or her preference information for particular products but rather capture his or her preferences from data that result from such activities. Information on the web usage behavior for the products determines the ordinal relationships among the products, which express that certain product is preferred to other products across the multiple aspects. The ordinal relationships among the products and the multiple aspects of products lead to the consideration of a multiple-criteria decision-making approach. Thus, the problem eventually results in the identification of weights attached to the multiple criteria in the multidimensional preference space constructed by the ordinal relationships among the products. The derived weights are then used for the prioritization of products that are not included in the navigation behavior due to factors such as time pressure, cognitive burden, and the like.     

Efficient environment management for distributed simulation of large‐scale situated multi‐agent systems

Multi‐agent systems have been proven very effective for the modelling and simulation (M&S) of complex systems like those related to biology, engineering, social sciences and so forth. The intrinsic spatial character of many such systems leads to the definition of a situated agent. A situated agent owns spatial coordinates and acts and interacts with its peers in a hosting territory. In the context of parallel/distributed simulation of situated agent models, the territory represents a huge shared variable that requires careful handling. Frequent access by agents to territory information easily becomes a bottleneck degrading system performance and scalability. This paper proposes an original approach to modelling and distributed simulation of large‐scale situated multi‐agent systems. Time management is exploited for resolving conflicts and achieving data consistency while accessing the environment. The approach allows a simplification of the M&S tasks by making the modeller unaware of distribution concerns while ensuring the achievement of good scalability and performance during the distributed simulation. Practical aspects of the approach are demonstrated through some modelling examples based on Tileworld. Copyright © 2014 John Wiley & Sons, Ltd.     

Neural network‐based multi‐agent approach for scheduling in distributed systems

A distributed system consists of a collection of autonomous heterogeneous resources that provide resource sharing and a common platform for running parallel compute‐intensive applications. The different application characteristics combined with the heterogeneity and performance variations of the distributed system make it difficult to find the optimal set of needed resources. When deployed, user applications are usually handled by application domain experts or system administrators who depending on the infrastructure provide a scheduling strategy for selecting the best candidate resource over a set of available resources. However, the provided strategy is usually generic, aimed at handling a wide array of applications and does not take into consideration specific application resource requirements. As such, an intelligent method for selecting the best resources based on expert knowledge is needed. In this paper, we propose a neural network‐based multi‐agent resource selection technique capable of mimicking the services of an expert user. In addition, to cope with the geographical distribution of the underlying system, we employ a multi‐agent coordination mechanism. The proposed neural network‐based scheduling framework combined with the multi‐agent intelligence is a unique approach to efficiently deal with the resource selection problem. Results run on a simulated environment show the efficiency of our proposed method. Several scheduling simulations were conducted to compare the performance of some conventional resource selection methods against the proposed agent‐based neural network technique. The results obtained indicate that the agent‐based approach outperformed the classical algorithms by reducing the amount of time required to search for suitable resources irrespective of the resource size. Copyright © 2016 John Wiley & Sons, Ltd.     

Whether a retailer should enter an e‐commerce platform taking into account consumer returns

Considering consumers are increasingly shopping online nowadays and the online sales market is dominated by e‐commerce giants, traditional retailers need to choose whether to enter e‐commerce platforms. Moreover, traditional retailers need to determine whether to offer offline return services considering online return services are very popular. To address these challenges, we explore a retailer's optimal offline return strategy and channel choice of whether or not to enter a platform in the contexts of symmetric information and asymmetric information, respectively. We present conditions for the retailer to share information. Interestingly, we find that the retailer in some conditions has no motivation to improve customer satisfaction rate of offline store. Most important, we find that the retailer's channel choice depends on the magnitude of the annual service fee that is affected by offline return strategy and asymmetric information, and the offline return strategy depends on the magnitude of the average residual value of returned products.     

A multi-agent recommender system for supporting device adaptivity in e-Commerce

Traditional recommender systems for e-Commerce support the customers’ activities providing them with useful suggestions about available products in Web stores. To this purpose, in an agent-based context, each customer is often associated with a customer agent that interacts with the site agent associated with the visited e-Commerce Web site. In presence of a high number of interactions between customers and Web sites, the generation of recommendations can be a heavy task for both these agents. Moreover, customers can navigate on the Web by using different devices having different characteristics that may influence customer’s preferences. In this paper we propose a new multi-agent system, called ARSEC, where each device exploited by a customer is associated with a device agent that autonomously monitors his/her behaviour. Furthermore, each customer is associated with a customer agent that collects in a global profile the information provided by his/her device agents and each e-Commerce Web site is associated with a seller agent. Based on the similarity existing among the global profiles the customers are partitioned in clusters, each one managed by a counsellor agent. Recommendations are generated in ARSEC as result of the collaboration between the seller agent and some counsellor agents associated with the customer. The usage of the device agents leads to generating recommendations taking into account the device currently used, while the fully decentralized architecture introduces a strong reduction of the time costs. Some experimental results are presented to show the significant advantages obtained by ARSEC in terms of recommendation effectiveness with respect to other well-known agent-based recommenders.     

Understanding Customer Trust in Agent-Mediated Electronic Commerce, Web-Mediated Electronic Commerce, and Traditional Commerce

Customer trust is important in agent-mediated electronic commerce, web-mediated electronic commerce, and traditional commerce. However, the meaning of customer trust in these contexts has not been clearly defined or fully delineated. This paper proposes a new trust model that differentiates between cognitive trust and emotional trust, defines customer trust in each type of commerce as cognitive trust and emotional trust in the various entities (e.g., website, computer agent) that make up a commerce context, and then compares customer trust across the three types of commerce. We propose that, first, emotional trust merits research, particularly in e-commerce. Second, both awareness of the known and awareness of the unknown will be higher in e-commerce than in traditional commerce; this will lead to lower cognitive trust and emotional trust in e-commerce. Third, the key to increase customer trust in e-commerce is to design and develop technologies to reduce the distance between a customer and each entity, thereby increasing awareness of the known and decreasing awareness of the unknown. Fourth, cognitive trust and emotional trust fall along a continuum with potentially asymmetric effects on customer dependence on entities in e-commerce (e.g., computer agent adoption). Finally, future research on customer trust in e-commerce is outlined.     

A strong user authentication scheme with smart cards for wireless communications

Seamless roaming over wireless network is highly desirable to mobile users, and security such as authentication of mobile users is challenging. Recently, due to tamper-resistance and convenience in managing a password file, some smart card based secure authentication schemes have been proposed. This paper shows some security weaknesses in those schemes. As the main contribution of this paper, a secure and light-weight authentication scheme with user anonymity is presented. It is simple to implement for mobile user since it only performs a symmetric encryption/decryption operation. Having this feature, it is more suitable for the low-power and resource-limited mobile devices. In addition, it requires four message exchanges between mobile user, foreign agent and home agent. Thus, this protocol enjoys both computation and communication efficiency as compared to the well-known authentication schemes. As a special case, we consider the authentication protocol when a user is located in his/her home network. Also, the session key will be used only once between the mobile user and the visited network. Besides, security analysis demonstrates that our scheme enjoys important security attributes such as preventing the various kinds of attacks, single registration, user anonymity, no password/verifier table, and high efficiency in password authentication, etc. Moreover, one of the new features in our proposal is: it is secure in the case that the information stored in the smart card is disclosed but the user password of the smart card owner is unknown to the attacker. To the best of our knowledge, until now no user authentication scheme for wireless communications has been proposed to prevent from smart card breach. Finally, performance analysis shows that compared with known smart card based authentication protocols, our proposed scheme is more simple, secure and efficient.     

A unified logical model for CBR‐based e‐commerce systems

This article examines new issues resulting from applying case‐based reasoning (CBR) in e‐commerce and proposes a unified logical model for CBR‐based e‐commerce systems (CECS) that consists of three cycles and covers almost all activities of applying CBR in e‐commerce. This article also decomposes case adaptation into problem adaptation and solution adaptation, which not only improves the understanding of case adaptation in the traditional CBR, but also facilitates the refinement of activity of CBR in e‐commerce and intelligent support for e‐commerce. It then investigates CBR‐based product negotiation. This article thus gives insight into how to use CBR in e‐commerce and how to improve the understanding of CBR with its applications in e‐commerce from a logical viewpoint. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 29–46, 2005.     

Autonomic configuration and recovery in a mobile agent‐based distributed event monitoring system

In this paper we present a framework for building policy‐based autonomic distributed agent systems. The autonomic mechanisms of configuration and recovery are supported through a distributed event processing model and a set of policy enforcement mechanisms embedded in an agent framework. Policies are event‐driven rules derived from the system's functional and non‐functional requirements. Agents in the network monitor the system state for policy violation conditions, generate appropriate events, and communicate them to other agents for cooperative filtering, aggregation, and handling. A set of agents perform policy enforcement actions whenever events signifying any policy violation conditions occur. Policies are defined using a specification framework based on XML. The policy enforcement agents interpret the policies given in XML. We illustrate the utility of this framework in the context of an agent‐based distributed network monitoring application. We also present an experimental evaluation of our approach. Copyright © 2006 John Wiley & Sons, Ltd.     

Q‐learning for continuous‐time graphical games on large networks with completely unknown linear system dynamics

In this paper, we consider the problem of leader synchronization in systems with interacting agents in large networks while simultaneously satisfying energy‐related user‐defined distributed optimization criteria. But modeling in large networks is very difficult, and for that reason, we derive a model‐free formulation that is based on a separate distributed Q‐learning function for every agent. Every Q‐function is a parametrization of each agent's control, of the neighborhood controls, and of the neighborhood tracking error. It is also evident that none of the agents has any information on where the leader is connected to and from where she spreads the desired information. The proposed algorithm uses an integral reinforcement learning approach with a separate distributed actor/critic network for each agent: a critic approximator to approximate each value function and an actor approximator to approximate each optimal control law. The derived tuning laws for each actor and critic approximators are designed appropriately by using gradient descent laws. We provide rigorous stability and convergence proofs to show that the closed‐loop system has an asymptotically stable equilibrium point and that the control policies form a graphical Nash equilibrium. We demonstrate the effectiveness of the proposed method on a network consisting of 10 agents. Copyright © 2016 John Wiley & Sons, Ltd.     

Distributed edge event‐triggered consensus protocol of multi‐agent systems with communication buffer

This paper proposes a distributed edge event‐triggered (DEET) scheme of multi‐agent systems via a communication buffer to reduce unnecessary update of controllers induced by fast information transmission. This edge scheme avoids a synchronous phenomenon in node event‐triggered mechanism, in which the triggering of one agent activates information transmission of all edges linked with this agent. Hence, the node event‐triggered scheme leads to unnecessary update of control protocols while the DEET provides a new approach without constrains on synchronous phenomenon of edge information exchange. That is, the communication on each edge is independent with other edges. In addition, we investigate another case where edge information transmission is subject to quantization and a quantized edge event‐triggered control protocol is proposed. Note that such a quantized protocol guarantees asymptotical consensus instead of bounded consensus in most of the existing literature. Meanwhile, both DEET and quantized edge event‐triggered schemes have nontrivial properties of excluding Zeno behavior. Furthermore, an algorithm is provided to avoid continuous event detection; hence, the communication traffic of the whole network is reduced significantly. Copyright © 2016 John Wiley & Sons, Ltd.     

A hybrid trust‐enhanced collaborative filtering recommendation approach for personalized government‐to‐business e‐services

The information overload on the World Wide Web results in the underuse of some existing e‐government services within the business domain. Small‐to‐medium businesses (SMBs), in particular, are seeking “one‐to‐one'' e‐services from government in current highly competitive markets, and there is an imperative need to develop Web personalization techniques to provide business users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e‐governments can support businesses on the problem of selecting a trustworthy business partner to perform reliable business transactions. In the business partner selection process, trust or reputation information is crucial and has significant influence on a business user's decision regarding whether or not to do business with other business entities. For this purpose, an intelligent trust‐enhanced recommendation approach to provide personalized government‐to‐business (G2B) e‐services, and in particular, business partner recommendation e‐services for SMBs is proposed. Accordingly, in this paper, we develop (1) an implicit trust filtering recommendation approach and (2) an enhanced user‐based collaborative filtering (CF) recommendation approach. To further exploit the advantages of the two proposed approaches, we develop (3) a hybrid trust‐enhanced CF recommendation approach (TeCF) that integrates both the proposed implicit trust filtering and the enhanced user‐based CF recommendation approaches. Empirical results demonstrate the effectiveness of the proposed approaches, especially the hybrid TeCF recommendation approach in terms of improving accuracy, as well as in dealing with very sparse data sets and cold‐start users. © 2011 Wiley Periodicals, Inc.     

AN INTEGRATED CLUSTERING‐BASED APPROACH TO FILTERING UNFAIR MULTI‐NOMINAL TESTIMONIES

Reputation systems have contributed much to the success of electronic marketplaces. However, the problem of unfair testimonies has to be addressed effectively to improve the robustness of reputation systems. Until now, most of the existing approaches focus only on reputation systems using binary testimonies, and thus have limited applicability and effectiveness. In this paper, We propose an i ntegrated CLU stering‐ B ased approach called iCLUB to filter unfair testimonies for reputation systems using multinominal testimonies, in an example application of multiagent‐based e‐commerce. It adopts clustering techniques and considers buyer agents’ local as well as global knowledge about seller agents. Experimental evaluation demonstrates the promising results of our approach in filtering various types of unfair testimonies, its robustness against collusion attacks, and better performance compared to competing models.     

A WEB‐BASED PERSONALIZED BUSINESS PARTNER RECOMMENDATION SYSTEM USING FUZZY SEMANTIC TECHNIQUES

The web provides excellent opportunities to businesses in various aspects of development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking “one‐to‐one” e‐services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e‐services. Recommender systems is an effective approach for the implementation of Personalized E‐Service which has gained wide exposure in e‐commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item‐based fuzzy semantic similarity and item‐based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users, particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF‐based recommendation approaches, namely sparsity and new “cold start” item problems.     

Kernel support for zero‐loss Internet service restart

Owing to long serving time and huge numbers of clients, Internet services can easily suffer from transient faults. Although restarting a service can solve this problem, information of the on‐line requests will be lost owing to the service restart, which is unacceptable for many commercial or transaction‐based services. In this paper, we propose an approach to achieve the goal of zero‐loss restart for Internet services. Under this approach, a kernel subsystem is responsible for detecting the transient faults, retaining the I/O channels of the service, and managing the service restart flow. In addition, some straightforward modifications to the service should be made to take advantage of the kernel support. To demonstrate the feasibility of our approach, we implemented the subsystem in the Linux kernel. Moreover, we modified a Web server and a CGI program to take advantage of the kernel support. According to the experimental results, our approach incurs little runtime overhead (i.e. less than 3.2%). When the service crashes, it can be restarted quickly (i.e. within 210 μs) with no information loss. Furthermore, the performance impact due to the service crash is small. These results show that the approach can efficiently achieve the goal of zero‐loss restart for Internet services. Copyright © 2006 John Wiley & Sons, Ltd.