How to take into account general and contextual knowledge for interactive aiding design: Towards the coupling of CSP and CBR approaches

The goal of this paper is to show how it is possible to support design decisions with two different tools relying on two kinds of knowledge: case-based reasoning operating with contextual knowledge embodied in past cases and constraint filtering that operates with general knowledge formalized using constraints. Our goals are, firstly to make an overview of existing works that analyses the various ways to associate these two kinds of aiding tools essentially in a sequential way. Secondly, we propose an approach that allows us to use them simultaneously in order to assist design decisions with these two kinds of knowledge. The paper is organized as follows. In the first section, we define the goal of the paper and recall the background of case-based reasoning and constraint filtering. In the second section, the industrial problem which led us to consider these two kinds of knowledge is presented. In the third section, an overview of the various possibilities of using these two aiding decision tools in a sequential way is drawn up. In the fourth section, we propose an approach that allows us to use both aiding decision tools in a simultaneous and iterative way according to the availability of knowledge. An example dealing with helicopter maintenance illustrates our proposals

On the automated extraction of regression knowledge from databases

The advent of inexpensive, powerful computing systems, together with the increasing amount of available data, conforms one of the greatest challenges for next-century information science. Since it is apparent that much future analysis will be done automatically, a good deal of attention has been paid recently to the implementation of ideas and/or the adaptation of systems originally developed in machine learning and other computer science areas. This interest seems to stem from both the suspicion that traditional techniques are not well-suited for large-scale automation and the success of new algorithmic concepts in difficult optimization problems. In this paper, I discuss a number of issues concerning the automated extraction of regression knowledge from databases. By regression knowledge is meant quantitative knowledge about the relationship between a vector of predictors or independent variables (x) and a scalar response or dependent variable (y). A number of difficulties found in some well-known tools are pointed out, and a flexible framework avoiding many such difficulties is described and advocated. Basic features of a new tool pursuing this direction are reviewed

Estimating software project effort using analogies

Accurate project effort prediction is an important goal for the software engineering community. To date most work has focused upon building algorithmic models of effort, for example COCOMO. These can be calibrated to local environments. We describe an alternative approach to estimation based upon the use of analogies. The underlying principle is to characterise projects in terms of features (for example, the number of interfaces, the development method or the size of the functional requirements document). Completed projects are stored and then the problem becomes one of finding the most similar projects to the one for which a prediction is required. Similarity is defined as Euclidean distance in n-dimensional space where n is the number of project features. Each dimension is standardised so all dimensions have equal weight. The known effort values of the nearest neighbours to the new project are then used as the basis for the prediction. The process is automated using a PC based tool known as ANGEL. The method is validated on nine different industrial datasets (a total of 275 projects) and in all cases analogy outperforms algorithmic models based upon stepwise regression. From this work we argue that estimation by analogy is a viable technique that, at the very least, can be used by project managers to complement current estimation techniques

High fidelity full sized human patient simulation manikins: Effects on decision making skills of nursing students

Background: The continued use of high fidelity full sized human patient simulation manikins (HF-HPSMs) for developing decision making skills of nursing students has led to growing research focusing its value on student learning and decision making skills. Methods: In October 2012, a cross-sectional survey using the 24-item Nurse Decision-Making Instrument was used to explore the decision making process of 232 pre-registration nursing students (age 22.0 + 5.4; 83.2% female) in Singapore. Results: The independent samples t-tests demonstrated three significant predictive indicators. These indicators include: prior experience in high fidelity simulation based on pre-enrolled nursing course (t = 70.6, p = .001), actual hands-on practice (t = 69.66, p < .005) and active participation in debrief (t = 70.11, p < .005). A complete experience based on role-playing followed by active discussion in debrief was a significant contributor to the decision making process (t = 73.6667, p < .005). However, the regression model indicated active participation in debrief as a significant variable which explained its development (t = 12.633, p < .005). Conclusions: This study demonstrated the usefulness of active participation in simulation learning for an analytic- intuitive approach to decision making, however active participation in debrief was a more important influencing element than role-playing. In situations where resources are limited for students to experience hands-on role-playing, peer reviewing and feedback on others’ experiences could benefit students, just as much. However, further study is warranted to determine the development of HF-HPSMs as a pedagogic tool for enhancing the decision making process of nursing students