Long years of experience in the field and sometimes in the lab are required to develop consultants. Texas A&M University recently has established a new method to develop a drilling expert system that can be used as a training tool for young engineers or as a consultation system in various drilling engineering concepts such as drilling fluids, cementing, completion, well control, and underbalanced drilling practices. This method is done by proposing a set of guidelines for the optimal drilling operations in different focus areas, by integrating current best practices through a decision-making system based on Artificial Bayesian Intelligence. Optimum practices collected from literature review and experts' opinions, are integrated into a Bayesian Network BN to simulate likely scenarios of its use that will honor efficient practices when dictated by varying certain parameters. The advantage of the Artificial Bayesian Intelligence method is that it can be updated easily when dealing with different opinions. To the best of our knowledge, this study is the first to show a flexible systematic method to design drilling expert systems. We used these best practices to build decision trees that allow the user to take an elementary data set and end up with a decision that honors the best practices.

This thesis considers how to teach novices to debug computer programs. The investigation is specifically aimed at students in first year of computer science at university. The goal is to find an effective method of improving the debugging ability of novice subjects. Novices are less effective than experts. Although weaker across the board, novices display a critical lack of strategic skills in the debugging domain. Novices become lost and discouraged easily. Novices do not make good use of program structure, do not use test cases intelligently, and do not track their progress. Even a slight improvement in these aspects has the potential to significantly improve performance. Expert debugging behaviour was examined. This provided the basis for a model of debugging to be used in instructing novices. Teaching materials were developed to instruct and support students in following the model. The model and training were then tested in both laboratory and classroom situations. Results were mixed, with a generally positive trend. The model by itself was not overly effective. When the model was accompanied by training materials containing examples and scaffolding, students who were willing to spend time examining the training usually showed some improvement. In a few cases, the student's debugging style was dramatically improved. The improved performance seems to come partially from improved skill and partially from motivation. The model and materials show potential, and are likely to be more effective when used in an environment that allows interaction, rather than the current paper-based, non-interactive tutorial.

Report (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Abstract. Includes bibliographical references (leaves 40-45). Also available via the Internet.

Thesis (M.S.)--Ohio University, June, 1989. / Title from PDF t.p.

Thesis (M.S.)--Rochester Institute of Technology, 1985. / Typescript. Includes bibliographical references (leaves 74-75).

Thesis--University of Florida. / Description based on print version record. Typescript. Vita. Includes bibliographical references (leaves 125-129).

Thesis (M.B.A.)--University of Hong Kong, 1996. / Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 1998. / Vita. Includes bibliographical references (leaves 179-186). Available also in a digital version from Dissertation Abstracts.

Revision of thesis --Stanford University, 1980. / Includes index. Bibliography: p. [165]-169.

Thesis (M.A.)--Rowan University, 2008. / Typescript. Includes bibliographical references.