This paper describes soft computing approaches for human-agent communications in the context of influencing decision making behavior. Several methods are illustrated including using a person’s predispositions and generalization techniques that allow issues to be viewed in a more favorable light with social interaction persuasion tendencies modeled with soft computing. The context of a robotic assistant for the elderly is used to illustrate the various communication techniques. Finally the related topic of negotiations using some the developed techniques is presented.

The ability for a product developer to successfully launch useful products is tied to the company’s product design strategies. Due to the complexity of perception and expectations on new product design from customers and the diverse perspectives of the product developer, any approaches without systematically analyzing these complex criteria to assess decision making on product design strategies are therefore deemed as inappropriate. A systematic approach to determine appropriate product design strategies based on the attributes of product design and customer expectations should thus be considered. This research focuses on the investigation of shape formulation process using evolutionary grammars (EG) and non-linear product design analysis with Kano model to refine product design strategies. Through the strategic integration of evolutionary grammars and Kano model, the complex effects on the evolutionary design process and product design strategies are revealed. In order to demonstrate the viability of the approach, experiments are described and a comparison analysis on three EG-Kano reference models is performed. Upon determining the appropriate EG-Kano reference model, the product developer can refine the product design strategies to suit the targeted market.

Ontologies are the backbone of intelligent computing on the World Wide Web but also crucial in many decision support situations. Many sophisticated tools have been developed to support working with ontologies, including prominently exploiting the vast array of existing ontologies. A system called ALIGN is developed that demonstrates how to use freely available tools to facilitate ontology alignment. First two ontologies are built with the ontology editor Protégé and represented in OWL. ALIGN then accesses these ontologies via Java’s JENA framework and SPARQL queries. The efficacy of the ALIGN prototype is demonstrated on a drug-drug interaction problem. The prototype could readily be applied to other domains or be incorporated into decision support tools.

With the Web 2.0 paradigm, a huge volume of Web content is generated by users at online forums, wikis, blogs, and social networks, among others. These user-contributed contents include numerous user opinions regarding products, services, or political issues. Among these user opinions, certain comparison opinions exist, reflecting customer preferences. Mining comparison opinions is useful as these types of viewpoints can bring more business values than other types of opinion data. Manufacturers can better understand relative product strengths or weaknesses, and accordingly develop better products to meet consumer requirements. Meanwhile, consumers can make purchasing decisions that are more informed by comparing the various features of similar products. In this paper, a novel Support Vector Machine-based method is proposed to automatically identify comparison opinions, extract comparison relations, and display results with the comparison relation maps by mining the volume of consumer opinions posted on the Web. The proposed method is empirically evaluated based on consumer opinions crawled from the Web. The initial experimental results show that the performance of the proposed method is promising and this research opens the door to utilizing these comparison opinions for business intelligence.

This article examines the improvements provided when multimedia information in traditional SCADAS are included in electric facility management and maintenance. Telecontrol use in the electric sector, with the fundamental objective of providing increased and improved service to the operators who manage these systems, is also described. One of the most important contributions is the use of an agent network that is distributed around the electric facility. Through the use of multi-agent technology and its placement in embedded systems, to the authors design a system with a degree of intelligence and independence to optimize data collection and provide reaction proposals for the operator. The proposed agent-based architecture is also reviewed in this article, as are the design of an example agent and the results obtained in a pilot experience using the proposed hardware platform.

With the development of Web 2.0 technologies, the sharing of photographs has increased. In this paper, the authors evaluate the art of photography, analyze how to develop intelligent photograph sharing system, and explaine the requirements of such systems. The authors present an architecture of an intelligent Web 2.0 based system and in future hope to add more modules for retention of users on the system. The system focuses on Web 2.0 usage, web mining for personalization service, and brings a different approach to collaborative filtering.

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.

Adaptive learning approaches support learners to achieve the intended learning outcomes through a personalized way. Previous studies mistakenly treat adaptive e-Learning as personalizing the presentation style of the learning materials, which is not completely correct. The main idea of adaptive learning is to personalize the earning content in a way that can cope with individual differences in aptitude. In this study, an adaptive learning model is designed based on the Aptitude-Treatment Interaction theory and Constructive Alignment Model. The model aims at improving students’ learning outcomes through enhancing their intrinsic motivation to learn. This model is operationalized with a multi-agent framework and is validated under a controlled laboratory setting. The result is quite promising. The individual differences of students, especially in the experimental group, have been narrowed significantly. Students who have difficulties in learning show significant improvement after the test. However, the longitudinal effect of this model is not tested in this study and will be studied in the future.

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.

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.