A methodology for developing Second Life environments using case-based reasoning techniques

Launched in 2003, Second Life is a computer-based pseudo-environment accessed via the Internet. Although a number of individuals and companies have developed a presence (lands) in Second Life, no appropriate methodology has been put into place for undertaking such developments. Although users have adapted existing methods to their individual needs, this research project explores the development of a methodology for developing lands specifically within Second Life. After researching and examining a variety of different software methods and techniques, it was decided to base this research project methodology on Case-Based Reasoning (CBR) techniques, which shares a number of synergies with Second Life itself. With some modifications, a web-based system was designed based on CBR to work in accordance with Second Life. Collecting and analyzing the feedback for the first version of the web-based system identified the adjustments and improvements needed. Therefore, from tracking its progress against previous specifications and future activity, an updated version of the CBR web-based system covering the latest changes and improvements of the tool was introduced. In addition to this, new functionalities have been added in the improved version in order to refine and develop the original prototype to become a highly effective SL development tool. New feedback platforms have been provided to facilitate the use of the system and to obtain results which are more closely related to the users recommendations. Through the feedback process, the tool is becoming ever more useful to developers of Second Life systems. This research project discusses the use of Case-based reasoning techniques and evaluates their application to the development of space within Second Life

Dynamic knowledge validation and verification for CBR teledermatology system

Objective: Case-based reasoning has been of great importance in the development of many decision support applications. However, relatively little effort has gone into investigating how new knowledge can be validated. Knowledge validation is important in dealing with imperfect data collected over time, because inconsistencies in data do occur and adversely affect the performance of a diagnostic system.Methods: This paper consists of two parts. First, it describes methods that enable the domain expert, who may not be familiar with machine learning, to interactively validate knowledge base of a Web-based teledermatology system. The validation techniques involve decision tree classification and formal concept analysis. Second, it describes techniques to discover unusual relationships hidden in the dataset for building and updating a comprehensive knowledge base, because the diagnostic performance of the system is highly dependent on the content thereof. Therefore, in order to classify different kinds of diseases, it is desirable to have a knowledge base that covers common as well as uncommon diagnoses.Results and conclusion: Evaluation results show that the knowledge validation techniques are effective in keeping the knowledge base consistent, and that the query refinement techniques are useful in improving the comprehensiveness of the case base

Partner selection in virtual enterprises

Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 200