Artificial Intelligence (AI) encompasses the following general areas of research: (1) automatic programming, (2) computer vision, (3) expert systems, (4) intelligent computer-assisted instruction, (5) natural language processing, (6) planning and decision support, (7) robotics, and (8) speech recognition. Intelligent library systems utilize artificial intelligence technologies to provide knowledge-based services to library patrons and staff. This paper examines certain key aspects of AI that determine its potential utility as a tool for building library systems. It discusses the barriers that inhibit the development of intelligent library systems, and it suggests possible strategies for making progress in this important area. While all of the areas of AI research indicated previously may have some eventual application in the development of library systems, this paper primarily focuses on a few that the author judges to be of most immediate significance--expert systems, intelligent computer-assisted instruction, and natural language applications. This paper does not discuss the use of AI knowledge-bases in libraries as subject-oriented library materials.

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The principal problem is the control of a nonlinear system with uncertainty. We will consider a robot manipulator system, which is nonlinear, and uncertain (unknown parameters and modeling errors). Our goals are to come up with a design of controllers that insure the stability of the system and provide robustness to parameters changes and modeling errors. We will use the theory developed for uncertain linear systems after carrying out an exact linearization of the original system. This linearization which is not an approximation, has been recently developed. The linear part of the controller has been designed so as to guarantee tracking and disturbance rejection. However, additional constraints resulting from the original nonlinear system have to be taken care of. Our design is tested by simulation on a two degree of freedom robot manipulator, which is simple enough to simulate but has all the properties of more general manipulators.

Artificial Neural Networks have been developed at Rice University for the forecasting of the Dst index from solar wind and Dst parameters. The one hour Dst index is an Earth based measurement of variations in the H-component of the magnetic field that is indicative of the strength of the ring current, and thus magnetic storms. Comparison of the neural networks' outputs to the OMNI dataset values of Dst will be presented. These results verify the success of the neural networks in predicting Dst. Network performance when predicting Dst two or more hours into the future and testing of MSFM output based on neural net Dst input for the August 1990 storm will be presented. Comparisons between MSFM equatorial particle fluxes and CRRES satellite observations show the MSFM 10 keV proton equatorial fluxes raise interesting questions about the MSFM's use of the Dst input parameter.

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The Tower of London (TOL) task has been widely used as a measure planning ability in cognitive and neuropsychological settings.  In the current study, healthy participants used different planning manipulations to preplan a sequence of moves to match a set of discs to a goal set, then to execute the moves following the plan.  The effect of different planning manipulations and trial (puzzle) types on accurate performance was explored across the four experiments in the current study.  The effect of the concurrent think aloud at different stages of the planning process in the TOL task was investigated in experiment 1.  The results showed that think aloud did not disrupt or enhance performance in the TOL task.  The effect of trial difficulty and the contribution of fluid intelligence to planning were explored in experiment 2.  Trial type had a greater effect on planning, moving and solving the task with the minimum number of moves.  Participants with high fluid intelligence tended to be faster in their planning, recall and execution of their planned moves, and solved the most demanding trial with fewer moves than did their counterparts.  The contributions of working memory and intellectual ability in solving a more demanding task such as the Tower of Hanoi (TOH), and its correlation with the Tower of London were examined in experiment 3.  The TOL and TOH tasks correlated significantly but only in terms of execution time.  It was found that higher intellectual ability and better spatial memory capacity contributed to some aspects of performance on the two Tower tasks.  In terms of the TOH task, the results revealed that prolonged planning time in  the demanding 5-TOH problem was associated with higher intellectual ability.  Moreover, the better ability to estimate the minimum number of moves required to solve the 5-TOH problem might be attributable to a higher intellectual ability and greater spatial memory capacity.

<p> With the rise of powerful mobile devices and the broad availability of computing power, <i>Automatic Speech Recognition</i> is becoming ubiquitous. A flawless ASR system is still far from existence. Because of this, interactive applications that make use of ASR technology not always recognize speech perfectly, when not, the user must be engaged to repair the transcriptions. </p><p> We explore a <i>rational user interface</i> that uses of machine learning models to make its best effort in presenting the best repair strategy available to reduce the time in spent the interaction between the user and the system as much as possible. A study is conducted to determine how different candidate policies perform and results are analyzed. </p><p> After the analysis, the methodology is generalized in terms of a decision theoretical framework that can be used to evaluate the performance of other rational user interfaces that try to optimize an expected cost or utility.</p>

The purpose of this qualitative study was to explore and describe nurse managers’ perceptions and use of emotional intelligence (EI) in their daily practice in two community hospitals in western Canada. Emotional intelligence can be defined as the “ability to monitor one’s own and other’s feelings and emotions, to discriminate among them and to use this information to guide one’s thinking and actions” (Salovey & Mayer, 1990, p. 189; Mayer & Salovey, 1997). Mayer and Salovey’s (1997) Four Branch Model of Emotional Intelligence was used as a conceptual framework to examine ten nurse managers experiences of how they perceive and use EI in daily practice. Data were collected over a 12 week period using interviews and analyzed using open coding to categorize and develop themes. Three major themes and several subthemes emerged from the data as important to nurse managers’ perceptions and use of EI. The two themes, Perceiving Emotional Intelligence and Managing Emotions were more evident than the third theme, Managing Relationships. This study demonstrates that nurse managers have the ability to perceive and use emotions in themselves and others. This is an important finding as nurse managers are expected to be leaders within the organization. The ability to develop relationships evolved from the data and was an important theme for participants in order to understand the interaction and relationship between self and other. This finding was important to participants yet the concept of relationships is missing in the Four Branch Model of Emotional Intelligence. Although there are nursing studies which explore emotional intelligence, there are no studies which examine nurse managers’ perceptions of EI and how they use EI in their daily practice. The findings of this exploratory, qualitative study contribute to a beginning understanding of nurse managers’ perceptions and use of EI in their daily practices.

This thesis explored the concurrent validity of the Quick Test (QT) as a brief individual measure of intelligence by correlating it with the Wechsler Preschool and Primary Scale of Intelligence (WPPSI). The subjects were 72 urban preschool children (15 black, 57 white) from Muncie, Indiana. The subjects ranged in age from 4½ to 6½ years, and were chosen from diverse socioeconomic backgrounds. Subjects were randomly assigned to two groups, each comprised of 36 females and 36 males. Tests were administered by two graduate students in clinical psychology and rotated to control for the effects of testing order. The correlation coefficients between the QT and WPPSI Full, Verbal and Performance Scale IQs were r = .64, r = .67 and r = .52, respectively. All correlations were significant at the .001 level. The strong correlation (r = .67) between the WPPSI Verbal and QT IQ suggests that the QT may be a good brief measure of verbal intelligence. The mean IQs for the WPPSI Full (102), Verbal (99), and Performance (106) were all significantly different (p < .001) from the QT mean IQ of 92. This seems to suggest that the WPPSI and QT mean IQs should not be used interchangeably. Standard deviations for the WPPSI Verbal, Performance, Full Scale, and QT IQs were 14.63, 16.85, 15.92, and 20.51, respectively. The present concurrent validity is strong enough to support the use of the QT as a brief individual measure of verbal intelligence.

<p>An evolutionary algorithm (EA) is a biologically inspired metaheuristic that uses mutation, crossover, reproduction, and selection operators to evolve solutions for a given problem. Learnable Evolution Model (LEM) is an EA that has an evolutionary algorithm component that works in tandem with a machine learner to collaboratively create populations of individuals. The machine learner infers rules from best and least fit individuals, and then this knowledge is exploited to improve the quality of offspring. </p><p> Unfortunately, most of the extant work on LEM has been <i>ad hoc </i>, and so there does not exist a deep understanding of how LEM works. And this lack of understanding, in turn, means that there is no set of best practices for implementing LEM. For example, most LEM implementations use rules that describe value ranges corresponding to areas of higher fitness in which offspring should be created. However, we do not know the efficacy of different approaches for sampling those intervals. Also, we do not have sufficient guidance for assembling training sets of positive and negative examples from populations from which the ML component can learn. </p><p> This research addresses those open issues by exploring three different rule interval sampling approaches as well as three different training set configurations on a number of test problems that are representative of the types of problems that practitioners may encounter. Using the machine learner to create offspring induces a unique emergent selection pressure separate from the selection pressure that manifests from parent and survivor selection; an outcome of this research is a partially ordered set of the impact that these rule interval sampling approaches and training set configurations have on this selection pressure that practitioners can use for implementation guidance. That is, a practitioner can modulate selection pressure by traversing a set of design configurations within a Hasse graph defined by partially ordered selection pressure. </p>