Current and future information systems require a better understanding of the interactions between users and systems in order to improve system use and, ultimately, success. The use of personas as design tools is becoming more widespread as researchers and practitioners discover its benefits. This paper presents an empirical study comparing the performance of existing qualitative and quantitative clustering techniques for the task of identifying personas and grouping system users into those personas. A method based on Factor (Principal Components) Analysis performs better than two other methods which use Latent Semantic Analysis and Cluster Analysis as measured by similarity to expert manually defined clusters.

The World Wide Web is an increasingly important data source for business decision making; however, extracting information from the Web remains one of the challenging issues related to Web business intelligence applications. To use heterogeneous Web data for decision making, documents containing relevant data must be located, and the data of interest within the documents must be identified and extracted. Currently, most automatic information extraction systems can only cope with a limited set of document formats or do not adapt well to changes in document structure, as a result, many real-world data sources with complex document structures cannot be consistently interpreted using a single information extraction system. This paper presents an adaptive information extraction system prototype that combines multiple information extraction approaches to allow more accurate and resilient data extraction for a wide variety of Web sources. The Amorphic Web information extraction system prototype can locate data of interest based on domain knowledge or page structure, can automatically generate a wrapper for a data source, and can detect when the structure of a Web-based resource has changed and act on this to search the updated resource to locate the desired data. The prototype Amorphic information extraction system demonstrated improved information extraction accuracy for the four different extraction scenarios examined when compared with traditional data extraction approaches

Blood product transfusion is a financial concern for hospitals and patients. Efficient utilization of this dwindling resource is a critical problem if hospitals are to maximize patient care while minimizing costs. Traditional statistical models do not perform well in this domain. An additional concern is the speed with which transfusion decisions and planning can be made. Rapid assessment in the emergency room (ER) necessarily limits the amount of usable information available (with respect to independent variables available). This study evaluates the efficacy of using artificial neural networks (ANNs) to predict the transfusion requirements of trauma patients using readily available information. A total of 1016 patient records are used to train and test a backpropagation neural network for predicting the transfusion requirements of these patients during the first 2, 2-6, and 6-24 h, and for total transfusions. Sensitivity and specificity analysis are used along with the mean absolute difference between blood units predicted and units transfused to demonstrate that ANNs can accurately predict most ER patient transfusion requirements, while only using information available at the time of entry into the ER.

Artificial intelligence programs operating in competitive domains typically use brute-force search if the domain can be modeled using a search tree or alternately use nonsearch heuristics as in production rule-based expert systems. While brute-force techniques have recently proven to be a viable method for modeling domains with smaller search spaces, such as checkers and chess, the same techniques cannot succeed in more complex domains, such as shogi or go. This research uses a cognitive-based modeling strategy to develop a heuristic search technique based on cognitive thought processes with minimal domain specific knowledge. The cognitive-based search technique provides a significant reduction in search space complexity and, furthermore, enables the search paradigms to be extended to domains that are not typically thought of as search domains such as aerial combat or corporate takeovers.

University admissions and business personnel offices use a limited number of resources to process an ever-increasing quantity of student and employment applications. Application systems are further constrained to identify and acquire, in a limited time period, those candidates who are most likely to accept an offer of enrolment or employment. Neural networks are a new methodology to this particular domain. Various neural network architectures and learning algorithms are analyzed comparatively to determine the applicability of supervised learning neural networks to the domain problem of personnel resource allocation and to identify optimal learning strategies in this domain. This paper focuses on multilayer perceptron backpropagation, radial basis function, counterpropagation, general regression, fuzzy ARTMAP, and linear vector quantization neural networks. Each neural network predicts the probability of enrolment and nonenrolment for individual student applicants. Backpropagation networks produced the best overall performance. Network performance results are measured by the reduction in counsellors student case load and corresponding increases in student enrolment. The backpropagation neural networks achieve a 56% reduction in counsellor case load.

. The modelling and measurement of expertise is a relatively new research area in artificial intelligence and cognitive science. Many domains do not have a formal method for evaluating expertise. When formal methods exist, they are frequently inefficient. Using an extension to the IAM program, a pattern recognition and acquisition method for evaluating the level of expertise for the domain of chess is developed. Chess players, as well as experts in other domains, use cognitive chunks of perceptual patterns to gain a cognitive economy that enables them to evaluate complex domain situations faster and more accurately than novices. The IAM program acquires a representative collection of the perceptual patterns demonstrated by a domain expert and uses those patterns to analyse skill level. A longitudinal study of a developing player and a comparison of the developing player to an established expert demonstrates the utility of the developed method for evaluating expertise.

Our research demonstrates the utility of incorporating pattern recognition with machine learning in adversarial domains. We use induction to recognize cognitive behavioral patterns of an adversary and use those patterns for tactical planning in the adversarial domain. Results of our methodology for the application domain of chess are presented.