Ontology-Based Shopping Agent for E-Marketing

Before Internet consumers make buying decisions, several psychological factors come into effect and reflect individual preferences on products. In this paper, the authors investigate four integrated streams: 1) recognizing the psychological factors that affect Internet consumers, 2) understanding the relationship between businesses’ e-marketing mix and Internet consumers’ psychological factors, 3) designing an ontology mapping businesses’ e-marketing mix with Internet consumers’ decision-making styles, and 4) developing a shopping agent based on the ontology. The relationship between businesses’ e-marketing mix and Internet consumers’ psychological factors is important because it can identify situations where both businesses and Internet consumers benefit. The authors’ ontology can be used to share Internet consumers’ psychological factors, the e-marketing mix of online business and their relationships with different computer applications.

Information Sharing Strategies in Business-to-Business E-Hubs

Studies show that supply chain structure is a key factor affecting information sharing. Business-to-business (B2B) e-hubs have fundamentally changed many companies’ supply chain structure, from a one-to-many to a many-to-many configuration. Traditional supply chains typically center around one company, which interacts with multiple suppliers or customers, forming a one-to-many structure. B2B e-hubs, on the contrary, usually connect many buyers and sellers together, without being dominated by a single company, thus forming a many-to-many configuration. Information sharing in traditional supply chains has been studied extensively, but little attention has been paid to the same in B2B e-hubs. In this study, the authors identified and examined five information sharing strategies in B2B e-hubs. Agent performances under different information sharing strategies were measured and analyzed using an agent-based e-hub model and practical implications were discussed.

Multiagent Based Selection of Tutor-Subject-Student Paradigm in an Intelligent Tutoring System

Intelligent tutoring systems (ITS) aim at development of two main interconnected modules: pedagogical module and student module .The pedagogical module concerns with the design of a teaching strategy which combines the interest of the student, tutor’s capability and characteristics of subject. Very few effective models have been developed which combine the cognitive, psychological and behavioral components of tutor, student and the characteristics of a subject in ITS. We have developed a tutor-subject-student (TSS) paradigm for the selection of a tutor for a particular subject. A selection index of a tutor is calculated based upon his performance profile, preference, desire, intention, capability and trust. An aptitude of a student is determined based upon his answering to the seven types of subject topic categories such as Analytical, Reasoning, Descriptive, Analytical Reasoning, Analytical Descriptive, Reasoning Descriptive and Analytical Reasoning Descriptive. The selection of a tutor is performed for a particular type of topic in the subject on the basis of a student’s aptitude.

Algorithm for Decision Procedure in Temporal Logic Treating Uncertainty, Plausibility, Knowledge and Interacting Agents

Our paper studies a logic UIALTL, which is a combination of the linear temporal logic LTL, a multi-agent logic with operation for passing knowledge via agents’ interaction, and a suggested logic based on operation of logical uncertainty. The logical operations of UIALTL also include (together with operations from LTL) operations of strong and weak until, agents’ knowledge operations, operation of knowledge via interaction, operation of logical uncertainty, the operations for environmental and global knowledge. UIALTL is defined as a set of all formulas valid at all Kripke-Hintikka like models NC. Any frame NC represents possible unbounded (in time) computation with multi-processors (parallel computational units) and agents’ channels for connections between computational units. The main aim of our paper is to determine possible ways for computation logical laws of UIALTL. Principal problems we are dealing with are decidability and the satisfiability problems for UIALTL. We find an algorithm which recognizes theorems of UIALTL (so we show that UIALTL is decidable) and solves satisfiability problem for UIALTL. As an instrument we use reduction of formulas to rules in the reduced normal form and a technique to contract models NC to special non-UIALTL-models, and, then, verification of validity these rules in models of bounded size. The paper uses standard results from non-classical logics based on Kripke-Hintikka models.

Multi-Agent Negotiation in B2C E-Commerce Based on Data Mining Methods

The Multi agent system (MAS) model has been extensively used in the different tasks of e-commerce like customer relation management (CRM), negotiation and brokering. For the success of CRM, it is important to target the most profitable customers of a company. This paper presents a multi-attribute negotiation approach for negotiation between buyer and seller agents. The communication model and the algorithms for various actions involved in the negotiation process is described. The paper also proposes a multi-attribute based utility model, based on price, response-time, and quality. In support of this approach, a prototype system providing negotiation between buyer agents and seller agents is presented.

A Semantic-Enabled Middleware for Citizen-Centric E-Government Services

Research efforts toward effective e-Government infrastructures have gained momentum, motivated mainly by increasing demands to improve citizen participation in public processes, promote social e-Inclusion, and reduce bureaucracy. One of the biggest challenges is providing effective techniques to handle the inherent heterogeneity of the systems and processes involved, making them interoperable. This paper presents a semantically enriched middleware for citizen-oriented e-Government services (CoGPlat), which facilitates the development and operation of new e-Government applications with higher levels of dynamism. It introduces the use of composition techniques based on semantic descriptions and ontologies. Requirements like autonomy, privacy and traceability are handled by applying policies that govern the interactions among services.

Effective Use of Information Systems/Technologies in the Mergers and Acquisitions Environment

In this study, the authors examine the effects of information systems/technologies (IS/T) on the performance of firms engaged in growth strategies based on mergers and acquisitions (M&A). A model derived from a resource-based theory of the firm is developed to predict the influence of IS/T on performance of firms. Data on the financial performance of 133 firms are used to gauge the impact of IS/T on various M&A objectives. The results suggest that IS/Ts implement M&A objectives that seek to increase overall efficiency better than those that seek to introduce new products or efforts to increase sales. Future studies to examine the process of introducing new products from resource-based theory are suggested.

A New Behavior Management Architecture for Language Faculty of an Agent for Task Delegation

In this paper, the authors describe a new architecture for the language faculty of an agent that fulfills the interaction requirements of task delegation. The architecture of the language faculty is based on a conceptualization of the language faculty of an agent and a definition of its internal state paradigm. The new architecture is behavior-management based and possesses self-management properties. This architecture is compared with existing abstract self-management architectures, which examines how the new architecture solves unresolved issues of older models. The architecture description is followed by a case study - Multilingual Natural Language Agent Interface for Mail Service, which illustrates its application.

A Model to Increase the Efficiency of a Competence-Based Collaborative Network

This paper provides a model based on the Multi Agent System (MAS) paradigm that acts as a methodological basis for evaluating the dynamics in a collaborative environment. The model dynamics is strictly driven by the competence concept. In the provided MAS, the agents represent the actors operating on a given area. In particular, the proposed agents are composed of three distinct typologies: (i) the territorial agent, (ii) the enterprise agent, and (iii) the public agent. Each agent has its local information and goals, and interacts with others by using an interaction protocol. The decision-making processes and the competencies characterize in a specific way each one of the different agent typologies working in the system.

Collusion-Free Privacy Preserving Data Mining

Distributed association rule mining is an integral part of data mining that extracts useful information hidden in distributed data sources. As local frequent itemsets are globalized from data sources, sensitive information about individual data sources needs high protection. Different privacy preserving data mining approaches for distributed environment have been proposed but in the existing approaches, collusion among the participating sites reveal sensitive information about the other sites. In this paper, the authors propose a collusion-free algorithm for mining global frequent itemsets in a distributed environment with minimal communication among sites. This algorithm uses the techniques of splitting and sanitizing the itemsets and communicates to random sites in two different phases, thus making it difficult for the colluders to retrieve sensitive information. Results show that the consequence of collusion is reduced to a greater extent without affecting mining performance and confirms optimal communication among sites.